World Agriculture Vol 5 No1

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a editorial board

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editors World Agriculture Editorial Board Patrons Professor Yang Bangjie, Member of the Standing Committee of the National People’s Congress of China.

(China) Lord Cameron of Dillington, Chair of the UK All Party Parliamentary Group for Agriculture and Food for Development. (UK) Maxwell D. Epstein, Dean Emeritus, International Students and Scholars, University of California, Los Angeles.(USA) Sir Crispin Tickell, GCMG, KCVO, formerly, British Ambassador to the United Nations and the UK’s Permanent Representative on the UN Security Council (UK) Managing Editor and Deputy Chairman Dr David Frape BSc, PhD, PG Dip Agric, CBiol, FSB, FRCPath, RNutr.

Mammalian physiologist Regional Editors in Chief Robert Cook BSc, CBiol, FSB. (UK) Plant pathologist and agronomist Professor Zhu Ming BS, PhD (China) President of CSAE & President of CAAE Scientist & MOA Consultant for Processing of Agricultural Products & Agricultural Engineering, Chinese Academy of Agricultural Engineering Deputy Editors Dr Ben Aldiss, BSc, PhD, CBiol, MSB, FRES. (UK) Ecologist, entomologist and educationalist Dr Sara Boettiger B.A. ,M.A.,Ph.D (USA) Agricultural economist Professor Neil C. Turner, FTSE, FAIAST, FNAAS (India), BSc, PhD, DSc, (Australia) Crop physiologist, Professor Wei Xiuju BS, MS, PhD (China) Executive Associate Editor in Chief of TCSAE, Soil, irrigation & land rehabilitation engineer Members of the Editorial Board Professor Gehan Amaratunga BSc, PhD, FREng, FRSA, FIET, CEng. (UK & Sri Lanka) Electronic engineer & nanotechnologist Professor Pramod Kumar Aggarwal, B.Sc, M.Sc, Ph.D. (India), Ph.D. (Netherlands), FNAAS (India), FNASc (India) Crop ecologist Dr Andrew G. D. Bean, BSc, PhD, PG Dip. Immunol. (Australia) Veterinary pathologist and immunologist Professor Phil Brookes BSc, PhD, DSc. (UK) Soil microbial ecologist Professor Andrew Challinor, BSc, PhD. (UK) Agricultural meteorologist Dr Pete Falloon BSc, MSc, PhD (UK) Climate impacts scientist Professor Peter Gregory BSc, PhD, CBiol, FSB, FRASE. (UK) Soil scientist Professor J. Perry Gustafson, BSc, MS, PhD (USA) Plant geneticist Herb Hammond, (Canada) Ecologist, forester and educator Professor Sir Brian Heap CBE, BSc, MA, PhD, ScD, FSB, FRSC, FRAgS, FRS (UK) Animal physiologist Professor Fengmin Li, BSc, MSc, PhD, (China) Agroecologist Professor Glen M. MacDonald, BA, MSc, PhD (USA) Geographer Professor Sir John Marsh, CBE, MA, PG Dip Ag Econ, CBiol, FSB, FRASE, FRAgS (UK) Agricultural economist Professor Ian McConnell, BVMS, MRVS, MA, PhD, FRCPath, FRSE. (UK) Animal immunologist Hamad Abdulla Mohammed Al Mehyas B.Sc., M.Sc. (UAE) Forensic Geneticist Professor Denis J Murphy, BA, DPhil. (UK) Crop biotechnologist Dr Christie Peacock, CBE, BSc, PhD, FRSA, FRAgS, Hon. DSc, FSB (UK & Kenya) Tropical Agriculturalist Professor R.H. Richards, C.B.E., M.A., Vet. M.B., Ph.D., C.Biol., F.S.B., F.R.S.M., M.R.C.V.S., F.R.Ag.S. (UK) Aquaculturalist Professor John Snape BSc PhD (UK) Crop geneticist Professor Om Parkash Toky, MSc, PhD, FNAAS, (India) Forest Ecologist, Agroforester and Silviculturist Professor Mei Xurong, BS, PhD Director of Scientific Department, CAAS (China) Meteorological scientist Professor Changrong Yan BS, PhD (China) Ecological scientist Advisors to the board Dr John Bingham CBE, FRS, FRASE, ScD (UK) Crop geneticist

Published by Script Media, 47 Church Street, Barnsley, South Yorkshire S70 2AS, UK

Editorial Assitants Dr. Zhao Aiqin BS, PhD (China) Soil scientist Ms Sofie Aldiss BSc (UK) Rob Coleman BSc MSc (UK) Michael J.C. Crouch BSc, MSc (Res) (UK) Kath Halsall BSc (UK) Dr Wang Liu. BS, PhD (China) Horiculuturalist Dr Philip Taylor BSc, MSc, PhD (UK)

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contents In this issue ...

n World Agriculture welcomes new Patrons

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David Frape

editorials: n In this issue Dr David Frape

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n New Technology and Climate Change In China: A Global Problem Professor Sir John Marsh

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n Climate change and the Curate’s Egg; The carbon budget, cycle and sinks Dr David Frape and Professor Andrew Challinor

7-10

scientific: n Bee decline, pollination and food production Professor John Hamblin.

11-18

n China’s cultivated land change and its carbon budget measurement 19-24 based on the system dynamics, Dongmei Jiang , Associate Professor Xiaoshun Li, Professor Zhengfu Bian, Professor Jinming Yan, Professor Futian Qu, Professor Xiaoping Shi, Professor Shaoliang Zhang, & Guancong He n Using climate information to support crop breeding decisions 25-42 and adaptation in agriculture Dr Pete Falloon, Dr Dan Bebber, Professor John Bryant, Dr Mike Bushell, Professor Andrew J Challinor, Professor Suraje Dessai, Professor Sarah Gurr & Dr Ann-Kristin Koehler n Impact of climate change on crop production and agricultural 43-49 engineering technological countermeasures used in mitigation in China Dr Zhao Aiqin, Professor Zhu Ming & Professor Wei Xiuju n The challenge of breeding for increased grain production in an era of global climate change and genomics Professor Wallace Cowling

50-55

expected future contributions: n Dr Penelope Bebeli – Landraces in Greece. n Dr Michael Turner – Seed policies in guiding seed sector development in the ‘post project era’. n Dr Andrew Bean – Navigating emerging infectious disease outbreaks: charting a course in the wake of an epidemic storm.

If you wish to submit an article for consideration by the Editorial Board for inclusion in a section of World Agriculture: a) Scientific b) Economic & Social c) Opinion & Comment or d) a Letter to the Editor please follow the Instructions to Contributors printed in this issue and submit by email to the Editor editor@world-agriculture.net Cover photo – f9photos – fotolia.com

Published by Script Media, 47 Church Street, Barnsley, South Yorkshire S70 2AS, UK

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editorials

World Agriculture welcomes new Patrons

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t gives me great pleasure on the behalf of the Editorial Board to welcome three Individuals to the patronage of World Agriculture (WA).

They join Sir Crispin Tickell who has been our patron for many years. Sir Crispin many of you will know was British Ambassador to the United Nations and the UK’s Permanent Representative on the UN Security Council.

His long experience, wisdom and profound knowledge of international affairs have been a great asset to WA and especially to me.

He is now joined by: Professor Yang Bangjie who is not

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only a member of the Government of China, but is also Vice President AllChina Environment Federation and as an engineer is a “giant” of Chinese agriculture.

He has been instrumental in the introduction of advice provided by papers in WA to Chinese agricultural policy for the benefit of Chinese and worldwide climate.

Lord Cameron of Dillington is Chair of the UK All Party Parliamentary Group for Agriculture and Food for Development. Lord Cameron is a farmer himself.

He was chairman of the Countryside Agency from 1999 – 2004. As a

member of the House of Lords, Lord Cameron’s expertise on African agriculture will be of enormous benefit to WA. Maxwell D. Epstein, Dean Emeritus, International Students and Scholars, University of California, Los Angeles.

Max is a past president of the United Nations Association of West Los Angeles.

He introduced us to Professor Glen MacDonald, who holds the John Muir Memorial Endowed Chair. Max has a profound knowledge of human relations and of the human psyche.

Glen and Max are currently undertaking a project for WA. David Frape

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editorials

In This Issue David Frape

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griculture is not only affected by climate but land use influences climate. In the current Issue we approach this subject from both viewpoints.

First, Falloon et al. (pp 25-42.) review recent uses of climate information to support crop breeding decisions and make recommendations for how these decisions might benefit.

Aiqin, Ming and Xiuju (pp 43-49.) measured the effect of climate change on crop production in China over the period between 1980 and 2010.

Despite a northward expansion in cultivatable land area in China, owing to climate warming, and an increased fertilization effect from the elevated concentration of atmospheric CO2, the resulting net crop yield loss in China, owing to the warming, change in pattern of precipitation and increased frequency and intensity of extreme weather, is estimated to be 5%-10% in the next 30 years.

Second, grassland and forestry act as carbon sinks. The cultivation of soil causes the oxidation of the organic matter it contains, so reducing its water-holding capacity and releasing CO2 to the atmosphere. The extent to which this occurs, depends on the crop and on the methods of cultivation – in the extreme, the system of “no cultivation� v deep

ploughing causes vastly different amounts of CO2 to be released. The importance of this can be visualized by learning of the measurements reported by Jiang et al. (pp 19-24.) who found that the conversion of the natural landscape to an urban one in China has introduced a loss of carbon sinks and an increase in GHG production at the approximate rates, respectively of 0.20 and 14.76 tonnes of carbon/ha annually, where industrial and urban building is replacing the natural environment.

The rise in GHG production is 74 times as great as the loss of soil sinks as a consequence this urbanization.

This, together with the rising human population with their increasing rate of fossil fuel combustion, accounts for the rapid global rise in GHG production. Not all is lost, however, we all like to see bees at work. Our garden is frequented by several species of wild bee, in addition to the honey bee, on occasion in spring there must be thousands present.

There is widespread concerns that falling honey bee numbers will lead to food shortages. In this Issue Hamblin reports (pp 11-18.) on agriculturally important and predominantly bee pollinated families (Brassica, Cucurbit and Rose), compared with cohorts having similar production over 119

crops. If bee pollination is obligate then reduced bee numbers would be expected to be limiting both current crop yields and yield increase over time. There is no evidence that this has occurred. So for these crops, at the least, under the conditions of the experiment, we should not be excessively concerned, agriculturally, about the decline.

With the rapid change in climate, the traditional time consuming methods of selection, used in plant breeding by geneticists, are becoming redundant. Geneticists have over the last few decades sought and employed several short-cut methods of plant breeding. Cowling (pp 5055.) has developed a novel approach to this problem. He has used the animal model to accelerate response to selection in S0 (F1) recurrent selection of a self-pollinating crop for a low-heritability trait in Pisum sativum, the annual plant species used

by Mendel. This is the first application of the animal model to cyclic selection in heterozygous populations of selfing plants.

This technique should help in the endeavour of geneticists to breed crops that keep pace with climate change, using the information garnered in the way proposed by Falloon et al. to which reference is made above.

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editorials

New Technology and Climate Change In China: A Global Problem Professor Sir John Marsh

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his edition contains articles on the expected effects of climate change on agricultural production in China and on the role of plant breeding in adapting to the evolving situation.

Preparing to live in a world where there is a substantial change in climate is one of the most important tasks for researchers, policy makers and those who invest in agriculture and food production. China accounts for 19% of the world population. As recently as 2002 China had a small net export balance in agricultural and food trade, by 2011 it had a deficit in excess of 40billion US$.

As real incomes continue to rise demand is likely to rise for more expensive and resource demanding food products. The consequences are of global significance. Changes in agricultural productivity as a result of climate change in China have far reaching consequences for producers and consumers everywhere.

China has invested in agricultural technology and research and has greatly increased domestic production but even so the increased level of demand cannot be met from domestic production alone. Environmental constraints seem likely to make further progress more difficult.

Climate change in China will increase production in some areas, as plants benefit from higher levels of atmospheric CO2. Elsewhere, loss of sunlight, shortages of water and the frequency of extreme weather events will reduce output. This article foresees the overall crop yield loss to be between 5% and 10%. As a member of WTO, China’s impact on world trade in agriculture is likely to outweigh that of existing importers. It has already made significant agricultural investments in Africa. There the potential for additional food production to meet both African needs and supply export markets is substantial. For this to be realised there is a need for political stability and, within agriculture, improved management, new technology and new capital. Such development, whilst essential to meet market demands will add the difficulty of restraining and

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adapting to global warming.

The thrust of the argument for government action on climate change is that the long run cost of continued ‘business as usual’ is huge and not recognized by the market.

As a result resources are used in ways that do not reflect their total cost to society. It is a classic example of market failure – the sort of issue economic policy is supposed to address. Relevant policies can address both the mitigation of climate change, by reducing greenhouse gas emissions and adaptation of production and consumption to changes that result from global warming. An effective response will involve major constraints on the established and preferred pattern of consumption-e.g. leading to a reduction in intensive animal production. This is counter to the aspiration of most countries, including the poorest to attain higher real incomes. Such higher incomes generate demand for both private and public goods and services.

Political realities will determine the extent to which policy can constrain consumption to lower levels of greenhouse gas emissions. The difficulty of implementing such policies is indicated by reactions to austerity policies during the recent financial crisis.

The outcome tends to be a series of gesture policies that have minimal, or even adverse, effect on the need to curtail emissions.

Wind farms, bio-energy and higher taxes on fossil fuels sound like relevant policies, but are nowhere near a solution. Nuclear fuel and fracking, that might ease the medium term threat, are resisted by a diversity of pressure groups. Stronger policies that tackle consumption patterns directly are inhibited by their social consequences – they bear most heavily on poorest people in developed economies and on countries with very low levels of real per capita income. There is a pressing need to devise and apply ways in which the real resource cost of production per unit of output is reduced. Such costs include not only the resources directly used, but also consequential costs of

damage to the environment and of social disruption the pattern of employment changes.

Much of the new technology in farming has arisen from developments in other sectors of the economy. For example developments in information technology, that may have originated in military research, alter the functioning of markets and the control of production processes. The scope for further improvement is substantial both by the better application of known technologies and by the introduction of new research based systems of production. The application of new methods and the research on which further progress depends demand substantial investment. Innovation thus hinges on the development of the economy as a whole. When economies slow, both the capacity and the willingness to invest are reduced. This raises the paradox that policies aimed at curbing consumption in the short run may inhibit the development of new, more efficient and less environmentally damaging processes in the long run.

Funding for research is vulnerable when economies slow. The role of the state in sustaining fundamental research and disseminating efficient discoveries becomes even more important when pressures on public spending are most acute. The papers in this edition show the urgency and the possibility of introducing less damaging systems of production. As the world seeks to respond to the challenge of climate change the response of production and consumption in China is of major importance. Other countries will learn from experience in China and China can benefit from experience elsewhere of the benefits and the problems of technologies that seek to minimise the environmental cost of feeding a growing and richer population.

The green party is wrong in many ways –by insisting on the development of more wind farms and stopping the consumption of fossil fuels now, it will depress the UK’s competitive edge and there will not be the income to spend on research into more efficient use of solar energy.

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editorials

Climate change and the Curate’s EggF; The carbon budget, cycle and sinks.

David Frape and Professor Andrew Challinor Keywords: Climate change, carbon sinks, GHG, food production.

See Appendix for definitions of terms of wind farms and of solar panels and used. their replacement, owing to wear and

M

att Ridley, writing in the Wall Street Journal (1), made a number of points justifying why the world should continue to burn fossil fuels at an increasing rate.

We contend he draws the wrong conclusions from the established evidence. Ridley fails to recognize the potential chronic, dire and irreversible consequences of burning fossil fuels at an ever increasing rate.

Certainly, we agree with Ridley, that fossil fuels are the cheapest source of energy. However, are all the likely costs, now and in the future, of a rising atmospheric CO2 concentration accounted in the ledger? Is rising atmospheric CO2e causing global warming and is this likely to be a continuous event, unless action is taken? Would a reduction in the rate of burning fossil fuels, with an eventual cessation, gradually halt this rise? If not, life on this earth, as we know it, would seem difficult to sustain. We contend the risks attached to inaction are unacceptable.

The purpose of this editorial is to assess whether Ridley’s article is misleading. His contention is that when there is no dramatic change in climate, or in the frequency of adverse environmental events, as climate fluctuates over relatively short periods of time, we should continue with ‘business as usual’. However, large short term variation in climate and in the frequency of adverse environmental events, may conceal a slow, but continuous trend in these factors. Our contention is that if this trend is not stemmed it may reach unbearable limits in some regions of the world. We present below a critique of Ridley’s primary points: “The one most often invoked today is that we are wrecking the planet’s climate. But are we?” (Ridley,2015).

When solar energy is used for conversion to electrical energy there should be as little fossil fuel energy used as is possible. The construction

tear, uses energy. More significantly, both sources provide a discontinuous and unpredictable supply. This means they have to be supported by an assured source. This source, if for example it is a coal fired power station, cannot be turned off when it is not needed. Moreover, the solar and wind sources ideally require a means of storing excess production, when they may produce more than is needed at any particular time.

Thus, we agree that the adoption of wind farms and solar panels, at least in their present form, in high northern latitudes is not a solution. Their cost may inhibit adequate expenditure on research to determine feasible and effective alternatives. However, we are interested to note the recent rapid decline in costs of “solar energy” and that research bodies, such as the Rockefeller Foundation and the University of Cambridge are concentrating work in this area of research (http://www.bbc.co.uk/news/worldus-canada-29310475, accessed April 29 2015).

“Most climate scientists remain reluctant to abandon the models and take the view that the current “hiatus” has merely delayed rapid warming. A turning point to dangerously rapid warming could be around the corner, even though it should have shown up by now.” (Ridley, 2015).

If land surface temperature is increasing with time, more slowly than some expected, there are two moderating factors to consider: (a) buffering by the deep ocean: the ocean’s upper layers, have a gigantic heat capacity, so are able to mop up solar heat (conceded by Ridley, 2015 “---the IPCC, have concluded that climate sensitivity is low because --------ocean-heat uptake”). There has been an increase in temperature of the top 700 m of the sea of 0.168oC since 1969 (2).

Partly as a consequence of this and in part as a consequence

of melting snow and ice on land (a process which also absorbs heat), the sea level rose by 1.7 cm per decade during the 20th century and has increased over 3.6 cm over a decade since 1993 (3). “There has been ------no acceleration of sea-level rise” (Ridley 2015). (b) Second, the failure for a large increase during the earlier years of this century is apparently not due to a failure of an effect of GHGs, but the 2000s witnessed a periodic solar output decline resulting in an unusually deep solar minimum in 2007-2009 (4).

The Greenland and Antarctic ice sheets have decreased in mass. Data from NASA's Gravity Recovery and Climate Experiment show Greenland lost 150 to 250 km3 (36 to 60 cubic miles) of ice per year between 2002 and 2006, while Antarctica lost about 152 km3 (36 cubic miles) of ice between 2002 and 2005 (5). This process absorbs heat and also the exposed land and sea surface reflects less back to space. The melting would account in a large part for the acceleration in sea level rise (3).

If these conclusions are correct, they would explain the failure of the Earth’s surface to warm nearly as fast as predicted over the past 35 years, “despite carbon-dioxide levels rising faster than expected” (Ridley, 2015).

The amount of carbon dioxide absorbed by the upper layer of the oceans is increasing by about 2 thousand million tonnes per year (2Gt/yr) and since the beginning of the Industrial Revolution it has led to an increase in the acidification of the top layers of the oceans by 30% (6 ; 7; 8, 9, www.pmel.noaa.gov).

The rise in atmospheric CO2 has two major effects on ocean biology: (a) warming lowers the ocean’s oxygen tension and (b) acting as a carbon sink, it leads to its acidification.

A discussion of the consequences of this are not part of this editorial, but they are a source of alarm to oceanographers and influence oceans as a source of food (10).

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editorials “Only in the 1970s and 1980s did scientists begin to say that the mild warming expected as a direct result of burning fossil fuels—roughly a degree Celsius per doubling of carbon-dioxide concentrations in the atmosphere— might be greatly amplified by water vapor and result in dangerous warming of two to four degrees a century or more.

That “feedback” assumption of high “sensitivity” remains in virtually all of the mathematical models used to this day by the U.N. Intergovernmental Panel on Climate Change, or IPCC” (Ridley, 2015).

Climate models are based on fundamental natural processes. They are in a constant state of testing by thousands of scientists in universities, research centres and operational weather and climate centres around the globe.

The tests involve observations and model improvement, compared in many different ways. The trend is towards increased fidelity in representing atmospheric and climatic processes. This is not surprising - the models contain fundamental physical, chemical and biological relationships that have been known for many years.

The very notion of “abandoning” the models implies a fundamental flaw has been found. It hasn’t.

Land and ocean mean surface air temperature continues to increase (11,), as shown by NASA data (4) (Fig. 1) taken from all three major global (land & ocean) surface temperature reconstructions show that Earth has warmed since 1880 (12, www.ncdc. noaa.gov/oa/) in both the northern and southern hemispheres. Most of this warming has occurred since the 1970s in the north, with the 20 warmest years occurring since 1981, of which 10 of the warmest were during the period 1996-2008 (13).

Global carbon emissions are increasing with time. Le Quéré1et al.

(14), reported that for the last decade available (2002–2011), CO2 emissions from fossil fuel combustion and cement production were 8.3 ± 0.4 GtC yr-1, and that they will increase to 9.9 ± 0.5 GtC in 2013, 3.0 percent above 2010 and 61% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the economy. Emissions from land cover change, deforestation and fire activity in regions undergoing deforestation were 1.0 ± 0.5 GtC yr-1. Thus, for the period, 1870–2013, about 70% of carbon emissions have come from burning fossil fuels and cement

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Fig. 1, Global Annual land and ocean mean Surface Air Temperature, (NASA, 2015)

production and 30% from land cover change. ”There has been no increase in the frequency or severity of storms or droughts, ----- the extra carbon dioxide in the air has contributed to an improvement in crop yields and a roughly 14% increase in the amount of all types of green vegetation on the planet since 1980.”( Ridley, 2015) Today we publish two papers from China which describe the effects, both potential and measured, of climate change on crop production in China.

Aiqin, Ming and Xiuju (pp 43-49) report crop yields between 1980 and 2010.

Despite a northward expansion in cultivatable land area in China, owing to climate warming, and an increased fertilization effect of CO2, the resulting net crop yield loss in China, owing to the warming, change in pattern of precipitation and increased frequency and intensity of extreme weather, is estimated to be 5%-10% in the next 30 years, based on their evidence.

“----despite carbon-dioxide levels rising faster than expected—the warming rate has never reached even two-tenths of a degree per decade---“ (Ridley 2015).

Over the past forty years the six Chinese regions have all been subject to a rise in surface temperature leading to a mean decadal change of +0.26oC, with a loss of sunshine and with an increase in the incidence of extreme weather; but precipitation has declined in the north and increased in the south. The average decadal values (Aiqin, Ming and Xiuju, pp 43-49) are: temperature (C) mean land surface +0.26o, range+ 0.18o to +0.38o

Precipitation (mm) -9.3 in the Northern regions, + 11.7 in the Southern regions Sunshine, (h) mean -58.5, range -24 to -119 Moreover, Ding Yihui et al. (2006) (15) measured China's warming trend over the last 50 years, during which the land surface air temperature has increased 1.1oC, i.e. 0.22oC per decade. Thus, the values from the present report are for an increase of +0.26oC per decade over the last 30 years and a value of +0.22oC per decade over the past 50 years – these values are within the same error range – both exceeding 0.2oC; although the variation is, as expected, considerable.

Over the last 100 years in China the decadal rate has been +0.08oC (16,), indicating a steep acceleration in the rate of over recent decades. “carbon dioxide in the air has contributed to an improvement in crop yields and a roughly 14% increase ------.Carbon-dioxide emissions should cause warming-------- a shifting northward the climate where cultivation was possible.”

(Ridley, 2015) An analysis of the impacts of climate change on agricultural production in China showed trends of temperature rise, sunshine decline and precipitation fluctuations overall. The frequency and intensity of extreme weather are increasing. The increased temperature has resulted in the northward and westward expansion of the cropping boundary, thereby increasing the area of arable land by 4.91% of the total in the Northeast from 1981 to 2010; in addition there has been a fertilization effect of CO2 to increase grain yield.

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editorials However, the temperature rise and precipitation decline aggravate drought, and water shortage in most parts of the north, thereby lowering the yields of maize, wheat and soybean, which together with an increased frequency and intensity of meteorological disasters in other regions, are expected to lower net crop yield in Chinaby 5%-10% in the next 30 years (Aiqin,Ming & Xiuju, pp 43-49.).

What is causing the increased rate of CO2e production?

The paper by Jiang, Li, Bian , Ming, Qu, Shi, Zhang & He. (pp 19-24) shows that as a result of industrialization an increasing proportion of China’s cultivated land is being urbanized, causing considerable environmental damage.

By modelling the carbon budget resulting from the industrialization and urbanization of farmland from 1996 to 2020, they have shown that the area of China’s cultivated land is decreasing, from 1.293 million km2 in 1996 to a predicted 1.204 million km2 in 2020, whereas 0.499 million km2 were likely to be built on by 2020 rising from 0.204 million km2 in 1996.

As a direct consequence the national carbon sink is likely to have decreased from 0.79 billion tonnes/an. in 1996 to 0.748 billion tonnes/an. in 2020, and GHG production is likely to have increased from 9.34 billion tonnes/an. in 1996 to 11.7 billion tonnes/an. by 2020.

A similar situation is occurring in most regions of the world- that is urbanisation of the natural environment is likely to be causing a loss of carbon sinks and an increase in GHG production at the approximate rates respectively of 0.20 and 14.76 tonnes/ha annually, where industrial and urban building is replacing the natural environment.

In addition there will be an increasing loss of both animal and plant species (biodiversity).

These developments are the result of an increasing world population and a general movement of people from the countryside to cities with greater social demands and increased consumption per capita with their increased rates of consumption of fossil fuels.

Conclusions The sensible conclusion is that the risks of an ever increasing atmospheric concentration of CO2 are too great to be accepted or even to be contemplated. Even without absolute

proof that rising atmospheric concentrations of GHGs are the cause of climate change and that this change is largely anthropogenic it seems very sensible to curb the use of fossil fuels, as soon as possible, otherwise the rise in atmospheric CO2e and temperature may reach limits which make normal human activity impossible. Slowing the rate does not mean the immediate cessation of the burning of fossil fuels, because research and development of alternatives have to be financed. The solutions require more serious international cooperation than occurs at present.

The rise in surface temperature and the decline in biodiversity are not only correlated with, but are very likely to have, an anthropogenic origin, i.e. there will be too many of us on this planet! It seems wise to voluntarily curb the growth of the human species.

It might otherwise be subject to a cataclysmic decline at some undeterminable future date, as occurs with most other species which outgrow their position on this planet.

F

Footnote: This term derives from a cartoon published in the British magazine Punch on 9 November 1895.

Drawn by George du Maurier and entitled True Humility, it pictures a timidlooking curate eating breakfast in his bishop's palace. The bishop remarks with candid honesty to his lowly guest: "I'm afraid you've got a bad egg, Mr Jones." The curate replies, desperate not to offend his eminent host and ultimate employer: "Oh, no, my Lord, I assure you that parts of it are excellent!"

Appendix

Carbon Unit Equivalents

UK Legislation (Climate Change Act 2008 ) refers to carbon budgets in terms of carbon unit equivalents, i.e. including all major greenhouse gasescarbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulphur hexafluoride (SF6) and any other greenhouse gas added later. Carbon dioxide equivalency is a quantity that describes, for a given mixture and amount of greenhouse gas, the amount of CO2 that would have the same global warming potential (GWP), when measured over a specified timescale (generally, 100 years), described as CO2e. For example, the GWP for methane is 25 and for nitrous oxide 298.

This means that emissions of 1 million

metric tonnes of methane and nitrous oxide, respectively, are equivalent to emissions of 25 and 298 million metric tonnes of carbon dioxide i.e. CO2em.

Many documents use CO2ev. This refers to an equivalent volume of gas which is similar for N20, but quite different for methane and for sulphur hexafluoride – for one volume of methane the equivalent volume of CO2, is not 25 but only 9.1.

The carbon cycle is the series of processes by which carbon compounds are interconverted between its major reservoirs—the atmosphere, oceans, and living organisms, involving the incorporation of carbon dioxide into living tissue by photosynthesis and its return to the atmosphere through respiration, the decay of dead organisms, and the burning of fossil fuels.

A carbon sink is a natural, or artificial, reservoir that accumulates and stores some carbon-containing chemical compound for an indefinite period.

The process by which a carbon sink removes carbon dioxide (CO2) from the atmosphere to a carbon sink is known as carbon sequestration.

Forests, soils, oceans and the atmosphere all store carbon and this carbon moves between them in a continuous cycle. Presently the greatest sinks are areas of vegetation, especially forests, and the phytoplankton-rich sea – these absorb the carbon dioxide produced by the burning of fossil fuels.

The global carbon budget is the balance of the exchanges (incomes and losses) of carbon between the carbon reservoirs, or between one specific loop (e.g., biosphere and atmosphere) of the carbon cycle.

Fixation is a process of incorporating carbon dioxide into the molecules of living matter. Nearly all carbon dioxide fixation is accomplished by means of photosynthesis.

Photosynthesis is the process in which green plants, algae, and cyanobacteria utilize the energy of sunlight to manufacture carbohydrates from carbon dioxide and water in the presence of chlorophyll.

References 1 Matt Ridley, Wall Street Journal, March 13,( 2015) Fossil Fuels Will Save the World (Really).

2, S. Levitus,1 J. I. Antonov,1 T. P. Boyer,1 R. A. Locarnini,1 H. E. Garcia,1 and A. V. Mishonov1 (2009) Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L07608, doi:10.1029/2008GL037155 3, Church, J. A. and N.J. White (2006), A 20th century acceleration in global sea level rise, Geophysical Research Letters, 33, L01602, doi:10.1029/2005GL024826.

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editorials The global sea level estimate described in this work can be downloaded from the CSIRO website.

(Understanding global sea levels: past, present and future. John A. Church, Neil J. White, Thorkild Aarup, W. Stanley Wilson, Philip L. Woodworth, Catia M. Domingues, John R. Hunter and Kurt Lambeck) 4. Gavin A. Schmidt (2015) Website Curator: Robert B. Schmunk, pp. 1-2, page update 17/04/15 NASA, Goddard Institute for Space Studies 5, L. Polyak, et.al., “History of Sea Ice in the Arctic,” in Past Climate Variability and Change in the Arctic and at High Latitudes, U.S. Geological Survey, Climate Change Science Program Synthesis and Assessment Product 1.2, January 2009, chapter 7.

6 , Christopher L. Sabine, Richard A. Feely, Nicolas Gruber, Robert M. Key, Kitack Lee, John L. Bullister, Rik Wanninkhof, C. S. Wong, Douglas W. R. Wallace, Bronte Tilbrook, Frank J. Millero, Tsung-Hung Peng, Alexander Kozyr, Tsueno Ono, Aida F. Rios (2004) The Oceanic Sink for Anthropogenic CO2. Science, 305(5682), 367–371 7, Allison, I., Bindoff, N. L., Bindschadler, R. A., Cox, P. M., de Noblet, N., England, M. H., Francis, J. E., Gruber, N., Haywood, A. M., Karoly, D. J., Kaser, G., Le Quere, C., Lenton, T. M., Mann, M. E., McNeil, B. I., Pitman, A. J., Rahmstorf, S., Rignot, E., Schellnhuber, H. J., Schneider, S. H., Sherwood, S. C., Somerville, R. C. J., Steffen, K.,

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Steig, E. J., Visbeck, Martin and Weaver, A. J. (2011) The Copenhagen Diagnosis: Updating the World on the Latest Climate Science. Elsevier, Oxford, UK, 114 pp. 2. ISBN 9780123869999. page 36.

8, Neyssa Hays (2014) Demystifying Ocean Acidification: a Human Interest Story ag Archives: ocean acidification, Posted on January 23, 2014 2; http://www.pmel.noaa.gov/co2/story/What+is+Oc ean+Acidification%3F9, Anon. (2015) ARGUMENTATIVE, DIVISION/CLASSIFICATION pp. 3-10 IS GLOBAL CLIMATE CHANGE REAL? 9, 13.http://www.pmel.noaa.gov /co2/story/Ocean+Acidification 10, Brander, K.M. (2007) Global fish production and climate change, Proceedings of the National Academy of Science of the United States of America, 104 (50) December 11, 19709–19714, doi: 10.1073/pnas.0702059104.

11 , I. Allison, N.L. Bindoff, R.A. Bindschadler, P.M. Cox, N. de Noblet, M.H. England, J.E. Francis, N.Gruber, A.M. Haywood, D.J. Karoly, G. Kaser, C. Le Quéré, T.M. Lenton, M.E. Mann, B.I. McNeil, A.J. Pitman, S. Rahmstorf, E. Rignot, H.J. Schellnhuber, S.H. Schneider, S.C. Sherwood, R.C.J. Somerville, K. Steffen, E.J. Steig, M. Visbeck, A.J. Weaver. (2009). The Copenhagen Diagnosis: Updating the World on the Latest Climate Science, The University of New South Wales. Climate Change Research Center, Sydney, Australia. 60pp, p.11.

12, Richard W. Reynolds and Huai-Min Zhang

(2009) In Situ and Satellite Sea Surface Temperature (SST) Analyses. pp.1-5. NOAA National Climatic Data Center, Asheville NC FY2009 Annual Report [In Situ and Satellite SST Analyses] http://www.ncdc.noaa.gov/oa/climate/research/ anomalies/index.html http://www.cru.uea.ac.uk/cru/data/temperature; http://data.giss.nasa.gov/gistemp 13, T.C. Peterson et.al., (2009) “State of the Climate in 2008,”Special Supplement to the Bulletin of the American Meteorological Society, v.

90, no. 8, August 2009, pp. S17-S18.

14 , C. Le Quéré1, G. P. Peters2, R. J. Andres3, R. M. Andrew2, T. A. Boden3, P. Ciais4, P. Friedlingstein5, R. A. Houghton6, G. Marland7, R. Moriarty1, S. Sitch8, P. Tans9 et al. (2014). Global carbon budget 2013; Earth System Science Data, 6, 235-263.

15, , Ding Yihui, Ren Guoyu, Shi Guangyu, et al.

(2006) National assessment report of climate change (I): Climate change in China and its future trend. Advances in Climate Change Research 2(1):3-8.

16 Guoli Tang, Yihui Ding, Shaowu Wang Guoyu Ren,Hongbin Liu Li Zhang (2010) Comparative Analysis of China Surface Air Temperature Series for the Past 100 Years. ADVANCES IN CLIMATE CHANGE RESEARCH 1(1): 11-19. www.climatechange.cn DOI: 10.3724/SP.J.1248.2010.00011 Corresponding author: Guoli Tang, tanggl@cma.gov.cn

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Bee decline, pollination and food production John Hamblin ATSE, FAIAST, Adjunct Professor Institute of Agriculture, University of Western Australia, 35 Stirling Hwy Crawley, Western Australia 6009 john.hamblin@uwa.edu.au Summary There are widespread concerns that falling honey bee numbers will lead to food shortages. This view is not universally accepted and further information is needed to determine future directions to reduce the potential impact of bee decline.

Here I examine yield changes over 51 years for three agriculturally important and predominantly bee pollinated families (Brassica, Cucurbit and Rose families) and I compare them to their cohorts having similar production over 119 crops. To allow comparisons between different crops relative yield changes (percent) have been used as a surrogate for pollination success over time. In the two most important crop production cohorts (> 100 Million Tonnes (Mt) production per year and 20-100 Mt), the average yields of the three bee-requiring crop families are increasing faster, on a relative basis, than the cohort average. In the two smaller cohorts (1-20 Mt and < 1 Mt) the reverse occurs. Also the yields of the annual species in these three crop families are increasing rapidly, whereas those in perennial crops are not. These factors (scale of production and annual v perennial habit) are independent of a requirement for bee pollination. Reasons for these results and implications for food production are considered. If bee pollination is obligate then reduced bee numbers would be expected to be limiting both current crop yields and yield increases over time. There is no evidence that this has occurred.

Key words Bee decline; Pollination; Food Production

Glossary FAO Food and Agricultural Organisation of the United Nations.

Mt Million tonnes. t/ha tonnes/hectare.

Brassica Brassicaceae (formerly

Introduction; the current debate

T

here is considerable discussion in the scientific literature, news media and on the web about bee decline and its impact on pollination for food production.

The consensus view (1) is that pollinators, particularly honey bees, are declining and this has major implications for food production (1- 4 and many others).

Several reasons have been advanced for bee decline, but there is little agreement as to the prime cause or combination of causes (1- 5; 5 has a good layman’s summary).

Bee decline is reported widely in the media (6-8), leading to claims that “we can’t afford to gamble any longer with our food, countryside and economy” (9).

In these discussions little attempt has been made to consider the actual impact of reduced bee numbers on crop yields and production. In the UNEP document on emerging

Cruciferae).

Curcurbit Cucurbitaceae.

Rose Rosaceae.

UNEP United Nations Environment Programme.

Double Haploid Technology a method of producing true breeding

varieties in a short period of time.

GM Genetic Modification, a technology for introducing novel genes into an organism.

Parthenocarpic, Apomixis and Germline biology all refer to different reproductive strategies.

pollination issues (1), 58 references are quoted, but only one has production in the title – for seed of two legumes in prairie grasslands.

Ghazoul (10) in 2005 suggested that current bee decline levels were less threatening to food production than was apparent in the literature.

His views were immediately challenged (11). Although Ghazoul (12) replied, his suggestion has basically been ignored by the “bee world”.

Exceptions include Alzen et al. (13) and Cunningham (14) who examined the FAO database (15) for crop yields up to 2006 and found little evidence that yields are falling due to a lack of pollinators in either developed or developing countries.

FAO also provides data on honey production (16).

Analysis used. The 119 crops were split into cohorts based on their total production in 2011 (Table 1).

These were: Massive Crops (13 crops, > 100 Million Tonnes [Mt] produced in 2011), Major Crops (31 crops, 20-100 Mt), Minor Crops (45 crops, 1-20 Mt) and Miniscule Crops (30 crops, > 1 Mt).

Note this list of 119 crops does not include either industrial crops e.g.

Tobacco, or unspecified groups, e.g.

Fruit, tropical fresh, that are included in the FAO database (15).

My focus is on relative yield improvement of crop members of the Brassica (e.g. cabbage), Cucurbit (e.g. melon) and Rose (e.g. almond) families that are in the FAO database. These families and crop species (20 species in total) are identified in Table 1 by different coloured crop entries.

Many members of these crop families are widely recognised as having a high requirement for bee pollination for high yields (3, 4).

Methods and Materials Data source. I have also used the FAO database (15) over the 51-year period 1961 to 2011 for production and yields of 119 food crops, as well as the data for individual countries.

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Table 1: Crops from the FAO database (15) used in this study; arranged alphabetically and by 2011 production cohort (Mt). The three crop families examined here in detail are in different colours to allow their easy identification within cohorts.

Yield changes over 51 years for these crop families are compared to the average of all crops in the same production cohort (Table 2).

Absolute yield changes between crops cannot be compared, as a tonne of watermelons is not the same as a tonne of rice; however relative changes in yield over time (% change) are comparable and can be considered as a surrogate for pollination effectiveness (13).

I then consider the corresponding data for France and the USA, two countries where major bee losses are reported.

Also many species of these three crop families are widely grown in both France and the USA. It would be expected that bee decline would show its impact primarily on the yield of these widely grown beesensitive crop families (3,13,18,19), but have little effect on the cohort averages that include many other

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species that are either self- or windpollinated.

Results for world crop data Average yield changes over time between the five year periods 1961-65 and 2007-11 for the three high beerequiring families used here are compared to the average yield change of their production cohort in Fig. 1.

Average yields of five years were used to smooth out individual year effects.

The individual yield changes over time of each of the 20 members of the three bee- requiring families together with the cohort means are listed in Table 2.

Fig. 1 and Table 2 show that, as the production importance of a crop decreases, there is a consistent fall in the average yield improvement over time, but there is no corresponding relationship for total crop production

(Table 2).

The ten bee-requiring crops in the Massive and Major production cohorts all had positive yield increases over the 51 years (Table 2).

In the Massive group yield improvement for watermelons, the only bee requiring crop in this cohort, was 192%, whilst the cohort mean was 102% (Fig. 1).

Besides exceeding the mean, the percentage yield increase of watermelons was the largest in the cohort.

A similar result occurred in the major crops where the average yield increase of the cohort was 80%, but the beerequiring crops averaged 104% (Fig. 1).

For Cucumbers, pumpkins and rapeseed yields increased by over 200% (Table 2).

These were the only species of the 31 in this cohort to have such large increases.

In the Minor and Miniscule crops some yield changes for over time for the three bee-requiring families were positive, whilst for others yields decreased.

For the Minor cohort the average yield increase was 47%, but in the beerequiring group it was only 19% (Fig. 1 and Table 2); Cherry, Plum and Sour Cherry yields decreased (Table 2).

In the Miniscule cohort average yield increased by 39%, but for the beerequiring group it was a mere 13% (Fig. 1).

The yields of the annual bee-requiring crops increased (mustard and melon seed); whereas for the perennial species (quince and raspberry) they decreased (Table 2).

In the Massive and Major cohorts yield increases over the last 51 years were large, but the average increase was even greater for the ten species of the three bee-requiring families.

For the Minor and Miniscule crops, the reverse occurred.

The bee-requiring crops had low or negative yield changes over time and their average was less than that of their cohorts.

Table 2 also shows that there are large differences between perennial and annual crops.

For the ten perennial crops (marked â&#x20AC;&#x2DC;+â&#x20AC;&#x2122; in Table 2) average yield increase over 51 years was an insignificant 3%, ranging from +78% for almonds to 64% for plums. In the ten annual crops (including the crops grown as annuals: Cabbages, Cauliflowers and Strawberries), the average yield increase was 122%,

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scientific ranging from 222% for pumpkins to 25% for cauliflower. The yields of these ten annual bee-requiring crops increased faster than the average yield increase of the 13 species in the massive cohort. Interactions occurred between cohorts. There were no negative yield changes for the ten beerequiring crops in the Massive and Major crop cohorts whereas in the ten Minor and Miniscule crops they were positive for annual, but were negative for some, but not all, of the perennial crops (Table 2).

World crop data discussion Why is the size of the crop important?

Fig 1. Percentage mean yield change between 1961-65 and 2007-2011 for the four production cohorts (>100Mt, 20-100Mt, 1-20Mt and <1Mt in blue) and the corresponding percentage mean yield change for the 3 bee requiring families in each cohort (in red)

Scale matters because large crops attract more funding, both public and private, for research, development and extension.

This phenomenon has long been recognised: “For he that hath,to him shall be given: and he that hath not, from him shall be taken even that which he hath”(17). Clearly the relative rates of change in crop yields over time and crop importance are related. This greater resource allocation to large crops has produced a high rate of yield improvement whilst for small crops the reverse is true.

Why are annual crop species at such an advantage?

Table 2: Percentage (%) change in mean world crop yields and production per cohort (Massive, Major, Minor &amp; Miniscule) and the actual yields (t/ha) and production (Mt) and their percentage improvement for the 20 crops of the Brassica, Cucurbit and Rose families examined here for the 5 year periods 196164 and 2007-2011.

Annuals are “easy” to breed: in one year useful data from several sites can be obtained. In some annual crops breeding can be accelerated by growing several generations/year and/or by using double haploid technology. Rapid seed multiplication means farmers get new varieties quickly and cheaply. Also any new technologies for growing crops can be rapidly introduced into farmers’ fields, sometimes within a year.

The extra cash flow generated from innovation is immediately available to farmers. None of these advantages occur in perennial crops. It is often several years before trees flower and even more before their yield and quality can be determined in a range of environments. Once orchards are established, it is often difficult to implement improved management techniques because of the initial planting arrangement. Increased cash flows from growing improved varieties or implementing improved technologies in perennial crops are often many years away from an initial, large investment.

Thus testing and then adopting new technology is riskier for farmers growing perennial than for those

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scientific growing annual crops. Neither of the factors ‘scale of production’ or ‘perennial versus annual habit’, is beedependent and it is highly unlikely that, on a world scale, bees are limiting production of the 20 bee-dependent crops examined here. The world honey data supports this, as production has increased nearly threefold in the last 51 years (16).

If bee decline was affecting world yields, then on average, yield increases over time would be expected to be less for the three bee-requiring crop families examined here than for the other crops in their cohort. Also, on average, yields of bee-dependent crops would be expected to be affected equally in all crop cohorts or even more so in the massive and major cohorts as more bees are required to pollinate the much larger areas of these crops (3).

Similar expectations apply to the differences between perennial and annual crops. If bee pollination is limiting yield, then on average, it should apply equally to both large and small crops and to annual and perennial ones. Clearly it does not.

Fig. 2 France, seed Brassica crop (mustard seed and rapeseed) yields (t/ha) for the 51 years 1961-2011

Results for two countries reporting bee decline

Country crop data for France and the USA

Both France and the USA are countries where there is considerable concern about bee decline and where several Brassica, Cucurbit and Rose crops are grown (4,15,18 & 19).

This combination provides the opportunity to examine, on a more local scale, the importance of bee decline on crop yields, using country data from the FAO database (15). The Brassica, Cucurbit and Rose family yield data for France are in Figures 2-6.

The corresponding data for USA are in Figs 7-11. Note y axis scales vary and that for Rapeseed, the USA data is only available from 1986, and therefore has not been included in the Figures, whereas it has for France (Fig. 2). Figs 2-6 provide little indication of recent, systematic reductions of crop yields in France for bee-sensitive crops over time.

The yields of Brassica grain crops (Mustard seed and Rapeseed) improved over time (Fig. 2).

In the case of cabbages and cauliflowers (Fig. 3), seed is only needed to plant crops as the parts eaten are harvested pre-flowering.

It is hard to imagine situations where seed producers are unable to manage pollination requirements for seed

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Fig 3. France, vegetable Brassica crop (cabbages and cauliflower) yields (t/ha) for the 51 years 1961-2011

Fig 4. France, Cucurbit crop yields (cucumbers, melons and pumpkins) for the 51 years 1961-2011

production even if costs are increased.

Note that seed production values for these two crops are commercially significant for seed companies and

details are not available to the public.

Variations in the yields of vegetable Brassicas crop yields are beeindependent. These comments also

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Fig 5. France, Rose crop yields (apples, apricots, pears and strawberries) for the 51 years 1961-2011

Fig 6. France, Rose crop yields (cherries, peaches, plums and raspberries) for the 51 years 1961-2011

apply to Fig. 8 for the USA data.

In Cucurbit crops (Fig. 4) yields rose for many years, but there has been a dramatic, recent drop in cuccumber yields and a fall in yield of both pumpkins and watermelons between2009-11.

In the Rose crops (Figs 5 & 6) the yields of Apricots, Cherries and Raspberries show little change over time, whereas the yields of all the other species have increased for the last 30 years.

The picture is even more clearcut in the USA (Figs 7,8,9 &10).

All crops have shown an increase in yield over the time, some very large indeed (both types of melon, (Fig. 8); apples, (Fig. 9); pears and strawberries, (Fig. 10). The only Brassica seed crop reported for the USA is rapeseed.

The data is for the period 1986-2011 and is not shown here as it is an inadequate times series, but shows an upward trend (15).

Discussion of the data for France and the USA For the French bee-sensitive crops of the three families considered here, yields have either been stable or have increased over time (Figs 2, 3, 5 & 6).

The exception is in the cucurbit crops (Fig. 4) where yields increased rapidly up to and including 2007 and then for cucumbers the yields fell dramatically.

There was a similar, but smaller, decrease for pumpkins and watermelons, but the yield of other melons increased slowly over the

period (Fig. 4).

When the three years (2005-2007) are compared with the following three years (2008-2010) for production, area and yield of French cucumbers, there was only a minor change in total production (16% higher in the latter period) whereas there was a dramatic increase in area (271%) and a 57% fall in yield (15). Possible reasons for this are government policy and/or changed economic circumstances making high cost intensive production less profitable than less intensive systems.

It is unlikely to be due to bee decline as the results are not reflected in the other melons nor in the other beerequiring families examined here and grown in France.

Also on a world scale, cucurbit yields have increased dramatically over the last 30 years (Table 2 & 15).

During this period honey production in France has been more or less constant at about 15,000t/annum (16). Despite a major web search I have been unable to find an explanation for this result.

The USA data show no impact of a lack of pollinators on production (Figs 7-10) in the three bee-requiring families examined here, with many crops showing large increases in yield over the 51 years even though, at the same time, USA honey production has halved (16).

The slight down-turn in almond yields (a crop of great concern for growers and bee keepers (4,19 & Fig. 9) reported for 2011 did not occur, as more recent data [USDA survey of Californian grower yields, (20) for 1995-2012], shows a continuing upward trend, with 2011being the highest yielding year recorded (Fig. 11).

The year to year variation in yields shows no consistent change associated with bee decline â&#x20AC;&#x201C; it is most likely due to seasonal conditions.

General Discussion

What are the options for managing a potential but as yet unrealised impact of bee decline on food supply?

There are four broad options: beecentric, crop-centric, farmer-centric and GM-centric.

Bee-centric

Reasons for bee decline are not yet clearly identified and many possibilities have been suggested (1, 4). Ways to avoid, manage, reduce or cure the factors involved in bee decline and their interactions need to be understood.

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scientific and optimised at a local scale. This should include “clean bee colony production systems” similar to those for broiler chickens where populations “crash” every few months and standard production practices ensure that chickens are completely, quickly, healthily and cheaply replaced.

The development of such systems for bees would mean that bees would always be available for distribution to bee keepers and crop growers as and when needed. This may require attitudinal changes by both amateur and commercial bee keepers, scientists and crop growers to develop effective and consistent, managed colony replacements.

Fig 7. USA, vegetable Brassica crop yields (cabbages and cauliflower) for the 51 years 1961-2011

Fig 8. USA, Cucurbit crop yields (cucumbers, melons and water melons) for the 51 years 1961-2011

Fig 9. USA, Rose crop yields (almonds in shell [c.f. Fig. 11 the yield is shelled almonds], apples, apricots, cherries and peaches) for the 51 years 1961-2011

This is an area where bee science and other skills are currently focused. Bee management needs to progress from semi-domestication to full

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domestication so that, as with other farm animals, nutrition, health, management, genetics, breeding and production regimes are understood

Crop-Centric

In 1922 Vavilov (21) proposed the law of homologous variation. This states that if a characteristic exists in one species, it is highly likely to exist in related species. Within the Brassica family there is much confusion about the level of cross- and self-pollination required in rapeseed (Table 3). This may be due to confusion between pollination studies involving Brassica napus and B. rapa both being reported as rapeseed to explain reported bee pollination requirements that vary from 0 to 100%! (Table 3).

Rapeseed breeders, when growing plants of B. napus in isolation cages to maintain pure lines, report considerable variation between varieties in their ability to selfpollinate. Should bees disappear, there will be immediate elimination of high bee-requiring rapeseed varieties in both farmers’ fields and breeding programmes, as they will be lowyielding. High-yielding replacement varieties of B. napus with low or zero bee pollination requirements already exist and are widely grown. If sought, this self-pollination capability is likely to be found in all Brassica crops (21). In the cucurbit family male and female flowers are on separate parts of the same plant but plants are selfcompatible. The issue is whether fully self-fertile varieties with hermaphrodite flowers can be bred that are highyielding and meet market requirements. Hermaphrodite flowers, controlled by a recessive gene, occur in both water- and other melons (22, 23). If selected for, this characteristic will improve rapidly and is likely to be available in all cucurbit crop species (21). Selection for self-fertility is simple, and like rapeseed requires exclusions cages to keep bees and other pollinators out.

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scientific would require a long term commitment of people, facilities and funding to be successful. In dieoecious crops where male and female flowers are on different plants (e.g. pistachio, kiwi-fruit) , this approach will be significantly more difficult to realise. Ultimately the path forward is a “simple” balance between product demand and the prices and costs of alternative strategies for pollination management. The genetics are not in doubt.

Farmer-centric

Fig 10. USA, Rose crop yields (pears, plums, raspberries, sour cherries and strawberries) for the 51 years 1961-2011

Farmers are very acute observers. For thousands of years they have made all the major advances in crop growing; species domestication, variety selection, irrigation, rotation, nutrition, pest control and adaptation to new locations. Farmers are many and crop scientists – particularly of minor crops – few. If made aware of the bee decline situation and its potential impact on their livelihoods, they would look for ways to improve their production. In these circumstances the chances of finding self-pollinating varieties, particularly in minor and miniscule crops, would increase significantly.

GM-centric

Fig 11. USA, almond yield (without shells, c.f. Fig. 9) 1995-2012 ex USDA (20)

As the plants are annuals, if no bees are available, fully fertile, hermaphrodite varieties will rapidly replace bee-requiring ones.

Parthenocarpic fruiting (not needing pollination to set fruit) occurs in cucumbers (23). Self-compatible types are available for all the rose family tree crops examined here (Table 4).

They are used by people wishing to grow a single fruit tree in city gardens.

Their value, for the reasons outlined earlier, to commercial growers is less certain.

Their ability to self fertilise is variable, but would be rapidly improved by selection.

As a fall back position, pre-breeding by public organisations to identify varieties of all three families that are fully self-fertile and near commercial for both yield and quality would pay long term dividends if the more pessimistic predictions for bee populations are realised.

This approach contrasts with that of Free (24) who also recommends a

crop-breeding approach, but to make flowers more bee-attractive, rather than bee-independent, as proposed here.

For reasons outlined earlier, breeding self-pollinating and self-fertile perennia crops will be slower and more expensive than for annual crops and

The production of complex biological molecules using microorganisms and GM are well established (insulin, rennet and vaccines).

Vanillin, the flavour of vanilla, is already made artificially without using a GM approach. In the worst-case scenario it would be possible to produce many plant flavours using GM technology. These products would be purer than those occurring in nature, but would lack local subtlety.

Alternative GM approaches might include developing plants with

Table 3: Web sites quoting different estimates for insect dependency for pollination of rapeseed. (all sites active 10.3.2014)

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Table 4: A small sample of sites promoting self fertile rose family fruit trees

apomixis or parthenocarpic fruits using germ-line biology to find genes that allow seed or fruit growth without pollination (25). However the non-GM options outlined above are likely to be both cheaper and less controversial.

Conclusions The suggestion (10, 12, 13 and 14) from nearly a decade ago that the “bee lobby” has over-stated the effect of bee decline on food production is correct. The focus has been on bees whilst yield, the other side of pollination equation, has largely been ignored. The data show that scale of production and annual v perennial habit have been the key drivers of yield increases or decreases in 20 crops from three bee-dependent plant families over the last 51years. These two factors are bee-independent and the rate of yield improvement in the annual species of the bee-requiring families examined here have been faster than their comparable crop cohort.

A lack of bees would be expected to affect yields of large crops more than small ones, whilst, in fact, the opposite was observed. Even in the worst-case scenario of near total, worldwide honey bee colony collapse, there are several alternative approaches to ensure these foods remain on the menu. The impact of bee-pollinator loss on biodiversity in wild plants and on honey production is likely to be far more severe than on agriculture.

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References 1. Kluser, S., Neumann, P., Chauzat, M-P., Jeffery S. Pettis. J.S. Global. Honey Bee Colony Disorder and Other Threats to Insect Pollinators. UNEP Emerging Issues: 1-16 (2010). @ http://www.unep.org/dewa/Portals/67/pdf/Global _Bee_Colony_Disorder_and_Threats_insect_pollina tors.pdf checked active 6.3.2014 2. Anon. Protecting the pollinators. FAO Spotlight: (Dec. 2005). @ http://www.fao.org/ag/magazine/0512sp1.htm checked active 6.3.2014 3. Rural Industries Research and Development Corporation. Pollination Aware – The Real Value of Pollination in Australia. Publication No. 10/081.

(2010). @ http://www.naturalbeekeeping.com.au/Pollination %20Aware.pdf checked active 6.3.2014 4. Benjamin, A., McCallum, B. A world without bees. Guardian Books, (purchase on line @ http://www.Guardianbooks.co.UK) 2008 checked active 6.3.14 5. Anon. Bees: decline in numbers. Royal Horticultural Society. @http://apps.rhs.org.uk/advice search/Profile. aspx?pid=528 checked active on 6.3.2014 6. Raskin-Zrihen, R. Solano, Napa honey producers concerned about bee declines. TimesHerald (12.7 2013) @ http://www.timesheraldonline.com/ci_23702532/ honeybee-population-decline-is-cause-concern checked active 6.3.2014 7. Beament, E. Urgent review launched into bee population decline. The Independent (28.06.2013) @ http://www.independent.co.uk/environment/natur e/urgent-review-launched-into-bee-populationdecline-8678624.html checked active 6.3.2014 8. Anon. Bee decline: Government announces &#039;urgent&#039; review. BBC (28.06.2013) @http://www.bbc.co.uk/news/uk-23090339 checked active 6.3.2014 9. Friends of the Earth, Executive Director Andy Atkins at a meeting organised by Friends ofthe Earth on bee decline 24.6.13 in London, and widely reported in the media e.g. 6 and 7 above 10. Ghazoul, J. Buzziness as usual? Questioning the global pollination crisis. Trends Ecol. Evol. 20, 367-373 (2005a)

11. Steffan-Dewenter, I. Potts, S.G. and Packer, L. Pollination diversity and crop pollination services are at risk. Trends Ecol. Evol. 20, 651-652 (2005) 12. Ghazoul, J. Response to Steffan-Dewenter el at.: Questioning the global pollination crisis.Trends Ecol. Evol. 20, 652-652 (2005b) 13. Alzen, M.A., Garibaldi,L.A., Cunningham, S.A., and Klein, A.M. Long term global trends in crop yields and production reveal no current pollination shortage but increasing pollinator dependency.

Current Biology 18, 1572-1575 (October 28 2008).

14. Cunningham, S.A. Honeybee decline warrants concern, but not panic. @ http://theconversation.com/search?q=Cunningha m+Bees checked active 6.3.2014 15. FAO crop database @ http://faostat.fao.org/site/567/DesktopDefault.asp x?PageID=567#ancor checked active 6.3.2014 16. FAO honey Database @ http://faostat.fao.org/site/569/default.aspx#ancor checked active 6.3.2014 17. King James Bible, Mark 4:25, but also see Matthew 13:12 and Luke 19:26 18. Anon. Pesticides Not Yet Proven Guilty of Causing Honeybee Declines, Experts Say. Science news. (20.09.2012) @ http://www.sciencedaily.com/releases/2012/09/12 0920141143.htm checked active 6.3.2014 19.Anon.Honey Bees and Colony Collapse Disorder USDA/ARS News. @ http://www.ars.usda.gov/news/docs.htm?docid=1 5572 checked active 6.3.2014 20. Annon. California Almond Forecast. USDA National Agricultural Statistics Service @ http://www.nass.usda.gov/Statistics_by_State/Calif ornia/Publications/Fruits_and_Nuts/201305almpd. pdf checked active 2.5.2013 21. Vavilov N.I. The law of homologous series in variation. J. Genetics. 12 (1) 47-89 (1922).

22. Purseglove, J.W. Tropical crops: Dicotyledons1, p112. Longman, Green and Co. Ltd London (1968) 23. Woodcock (2012) Pollination in the Agricultural Landscape @ http://www.pollinator.ca/canpolin/images/Pollinati on%20in%20Agricultural%20Landscape_Woodcoc k_Final.pdf checked active 6.3.2014 24. Free J.B. Insect pollination of crops. Second edition 1993. Academic Press Ltd. London 25. Hand L.M and Koltunow A.M.G. 2014 The Genetic Control of Apomixis: Asexual Seed Formation Genetics, June 2014 197:441-450; doi:10.1534/genetics.114.163105

Acknowledgements I would like to thank Freda Blakeway, Michael Chisholm, Dianne Davidson, William Erskine, James Ridsdill-Smith, Alistar Robertson, and Neil Turner for instructive comments on various drafts of this paper. The referee’s comments helped improve the focus and clarity of the paper.

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China cultivated land change and its carbon budget measurement based on the system dynamics Dongmei JIANG 1, Associate Professor Xiaoshun LI1, 2,3, Professor Zhengfu BIAN 1, Professor Jinming YAN2, Professor Futian QU3, Professor Xiaoping SHI3, Professor Shaoliang ZHANG1, and Guancong HE1 1. Key Laboratory for Land Environment and Disaster Monitoring of SBSM, China University of Mining & Technology, Xuzhou 221116 China; 2. Department of Land Management, Renmin University of China, Beijing 100872; 3.China Land Problem Research Center, Nanjing Agricultural University, Nanjing 210095, China Summary As a result of industrialization, an increasing proportion of China’s cultivated land is being urbanized, causing considerable environmental damage. In an endeavour to promote a low-carbon economy, this article focuses on modeling the carbon budget resulting from the industrialization and urbanization of farmland from 1996 to 2020, using empirical analysis and a dynamic system. The results show that, the area of China’s cultivated land is decreasing, whereas, land is being built on at a much faster rate. The analysis indicated that the area of cultivated land decreased from 1.293 million km2 in 1996 to 1.204 million km2 in 2020, whereas 0.499 million km2 were likely to be built on by 2020 rising from 0.204 million km2 in 1996.

The data indicated that, the rate at which cultivated land is lost is faster in the eastern region than in the central, western and north-eastern regions, indicating that the conversion is concentrated in the east of China. Carbon budget measurements indicate that nationally the carbon sink is likely to decrease from 0.79 billion tonnes in 1996 to 0.748 billion tonnes in 2020, and the carbon source is likely to increase from 9.34 billion tonnes in 1996 to 11.7 billion tonnes in 2020.

The loss of cultivated land finally indicates that the carbon budget is the largest in the east (5.03 billion tonnes, 48% of the national total), 2.28 billion tonnes in the west, 22% of the total, 2.17 billon tonnes in the central region, 22% of the total, and 1.08 billion tonnes in the northeast, 10% of the total. It is concluded that cultivated farmland should be continually protected to act as carbon sinks, and that it will be crucial to maintain a constant carbon budget. Government is required to strictly control industrial building on land, optimize energy efficiency, and retard carbon emissions.

Key words cultivated land conversion; carbon sink; carbon source; carbon budget; china

Introduction

M

ost countries have experienced the processes of industrialization and urbanization, with the consequential loss of cultivated land.

This land feeds mankind, so it is essential to a country's food security and social stability, functions which cannot be replaced. China’s cultivated land resources are scarce, possessing a relatively low per capita area (only 0.095 ha) of low quality (low and mid yield cropland accounted for 65%).

Cultivated land is decreasing annually from 1.301 million km2 in 1996 to 1.217 million km2 in 2008, a loss which was greatest in 2002 and 2003, when cultivated land area decreased respectively by 1.33 million ha and 2 million ha [1]. The rapid decline in land available for cultivation to a critical quantity of 1.2 million poses a serious threat to the country’s

economic development and food security. On October 23, 2008, the central government published the “A national land use planning outline, 2006-2020”, which proposes that in 2010 and 2020 there should be 1.212 million km2 and 1.203 million km2 of cultivatable land, respectively.

According to the evidence [1-6], with each 1% increase in urbanization, the city built area increases by 102 thousand ha, and cultivated land is reduced by 410 thousand ha out of a total of 0.122 billion ha of cultivated land in China. Thus industrialization and urbanization has caused a huge loss of cultivated land, threatening the food security of China.

There has been much research on the quantity and quality of cultivated land.

Its conversion to urban use has been of concern to policy makers and scholars since the 1990’s. The rural land requisition system[3], the current

cultivated land conversion process, a policy analysis of cultivated land conversion[4-6], the relationship of economic development to cultivated land conversion[7-11], and the relation between cultivated land conversion and food security[12-15], have all been discussed at length.

Publication of the “United Nations Framework Convention on climate change” and the “Kyoto Protocol”, on a low carbon economy become important topics for research, especially theoretical analyses on, for example, the carbon capture of specific plant species, the carbon economy coefficient of various soils[16-18], and carbon emissions of various soil and surface types[19-21]. Although the impact of cultivated land conversion on the local ecology has attracted attention, the mechanism by which the land conversion process influences the carbon sink and the carbon budget

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scientific is less well quantified.

In view of this, we focus on an analysis of cultivated land conversion as it affects a low carbon economy, the carbon sink, population (the whole country, the East, mid, West and northeast.), and economic development, using the method of system dynamics. The analysis should provide quantitative evidence to support policy decisions about nonagricultural land and the protection of China’s ecology.

2 Theory and Method 2.1 Theoretical analysis THE world resources organization’s carbon footprint calculator [22] and other estimations [16-21] indicate that between 1850 and 1998, global carbon emissions caused by land use change account for 1/3 of emissions derived from human activity; from 1950 to 2005, cumulative carbon emissions caused by Chinese land use changes was 10.6Pg, accounting for 30% of all man-made carbon emissions in China, and accounting for 12% of the global carbon emissions during that period.

From 1980 to 2005, China’s total urbanized land increased to 31.9 million ha, when the intensity and scale of carbon emission both grew rapidly. Construction land carbon emissions from land which has been built upon is several times to 100 times per unit area that of other land types.

Land use change is one of the important causes of an imbalance in the global carbon cycle, it is also the second most important human activity cause of increased atmospheric CO2 concentration, after that of the combustion of fossil fuels. As shown in the analysis of Figure 1, unreasonable land utilization reduces soil and

vegetation carbon storage, so that more carbon is released into the atmosphere, increasing the concentration of atmospheric CO2, so adding to global warming and the associated climate change. At present, cultivated land conversion is the main reason of irreversible change of land use. The farmland ecosystem is an important component of the terrestrial ecosystem carbon pool, a most active part, which is characterized by short periods of carbon fixation and large volumes. Green vegetation fixes carbon by photosynthesis, so acting as a carbon sink; but when soil is cultivated, or disturbed in land conversion, carbon stored in soil is released. Once cultivated land is built on, it becomes a carbon source. From 1996 to 2006, 8.27 million ha cultivated land was built on. The nonmarket value of ecology and food security are not taken into account, in the process of land conversion; hence the value of cultivated land is underestimated, so local government distorts land price, in favour of its use for buildings, leading to excessive demand for cultivated land as a resource for building, often accompanied with serious imbalance of carbon budget and a rapid growth of carbon emissions.

The measurement of carbon sink, carbon source and carbon balance caused by cultivated land conversion becomes an important basis for weighting of China’s ecology and environment. This paper defines the mechanism of carbon source sink and carbon budget change in the process of cultivated land conversion, using a system dynamics model for national, eastern, mid, western and northeastern regions. These areas are selected for an empirical quantitative analysis providing a decision-making basis for

policy innovation of China.

2.2 Model construction 2.2.1 Thought of study

The whole environment, its economic development and population change are regarded as part of a dynamic system. In this system, all the interactive elements have an effect on the change of cultivated land and its carbon budget of China.

The carbon budget of China, both regionally and nationwide, was subjected to an econometric analysis.

2.2.2 Introduction of method

A system dynamics was introduced by Forrest of the Massachusetts Institute of Technology in United States of America in 1956[23] to investigate complex systems by feedback control theory and an integrated qualitative and quantitative method.

It determines the structure and the dynamic behaviour of a carbon budget system by computer simulation[24-29].

For our analysis the three systems consist of farmland, construction land and the social economy.

It then simulates the dynamic behaviour of each system, and estimates by computer the rate of change of the carbon budget as the cultivated land is converted to building land.

2.2.3 Model

The cultivated land conversion carbon budget system contains many subsystems, including population, economic, grain and carbon sinks, carbon source systems, with mutual connections amongst them all (Fig. 2).

The data obtained from previous research [24-32] were analysed using system dynamics software Vensim, the cultivated land conversion carbon budget model of SD.

3 Empirical analysis 3.1 National empirical – overall judgment 3.1.1 Parameter estimation

Fig 1. The theoretical analysis framework of the cultivated land conversion carbon budget

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Population, countrywide cultivated land and construction land in 1996 were the parameters selected for this analysis with trend extrapolation fitting by statistical analysis.

For instance, the urbanization level was obtained from a city’s nonagricultural population as a proportion of its total population; the coefficient for the area of farmland used as construction land is taken as the national average according to “green residential area design code”.

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Fig 2. The flow chart of the cultivated land conversion carbon budget system model Note: Because new construction land mainly comes from the cultivated land in China, the relationship in the model between construction land and cultivated land is mainly established through the index of the occupied cultivated land proportion in the new construction land. For the regional differences, the proportions of the national, West, East, Center and Northeast are the average values over previous years.

The carbon sink, carbon source correlation coefficients were obtained from previous research results following adjustment [16-32]; construction land green area, ecological restoration, cultivated land carbon sink coefficients were 4.24, 8.08, 5.82, construction land green area, ecological restoration, cultivated land and construction land carbon source coefficients were 3.91, 6.38, 4.66, 361.59. The main parameters and mathematical logic relationship as shown in table 1.

3.1.2 Simulation results

According to the current trends of three variables which are people,

cultivated land and construction land, using system dynamics simulation tests model, we get the reliable simulation results.

The simulation is under a rapid economic development background?the cultivated land conversion speed is accelerated, and the low carbon economy is being promoted. The effect of cultivated land conversion’s on the carbon budget needs to be quantified. Computer simulation is used to simulate carbon source, carbon sink, carbon budget, and areas of cultivated land and construction land. The simulation results are shown in Fig. 3.

Table 1. The main parameters and mathematical logic relationship in simulation analysis

The predictions given in Fig. 3, are that the cultivated land decreases to 120.4 million hectares in 2020 from 129.33 million hectares in 1996 while the construction land increases to 30.73 million hectares from 24.07 million hectares.

The reduction of cultivated land is greater than the increase of construction land. The carbon sink decrease from 0.790 billion tonnes in 1996 to 0.748 billion tonnes in 2020.

The carbon source increases from 9.34 billion tonnes in 1996 to 11.7 billion tonnes in 2020. The carbon budget increases from 8.55 billion tonnes in 1996 to 11.0 billion tonnes in 2020.

The carbon sink reduction is proportionately less than is the loss of cultivated land. The carbon source increases proportionately faster than does the area of construction land. The carbon sink is declining and carbon source is increasing every year. Hence the carbon budget is predicted to decrease and the rate of loss to increase.

3.2 An regional comparison China can be divided into northeast, east, center and west four regions (Fig. 5). According to the above research methods for national, we simulated the values of the cultivated land and construction land in four regions from 1996 to 2020 (Fig. 4).

Then through computer simulation technology, simulate the carbon source, carbon sink, carbon budget amount in different year (Fig. 5).

The Fig. 4 shows that, cultivated land area in our country presents decreasing trend on the whole, construction land presents increasing state. And cultivated land conversion speed in our country presents east> middle > East West > northeast, which reflects our country’s cultivated land conversion key area is concentrated in the eastern region.

As shown in Fig. 5, the simulation values of carbon sink will be decreasing in four regions, and the carbon source will be increasing significantly in future. The carbon budget deficit is predicted to increase, but the increase is not huge. The carbon source and carbon budget in east will increase mostly. The carbon sink, carbon source and carbon budget will be stable in northeast.

According to Fig. 6, the proportion of Eastern region’s Carbon Budget grows continuously, from 42% in 1996 to 48% in 2020; the Northeast has been proportionately in a steady state, accounting for 10%; whereas, owing

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Fig 3. The chart of simulation results

Fig 4. The simulation value chart of the cultivated land and construction land in four regions

Fig 5. The regional carbon sink, carbon source and carbon budget in 1996, 2010 and 2020. As shown in Fig. 5, the simulation values of carbon sink will be decreasing in four regions, and the carbon source will be increasing significantly in future. The carbon budget deficit is predicted to increase, but the increase is not huge.

The carbon source and carbon budget in east will increase mostly. The carbon sink, carbon source and carbon budget will be stable in northeast.

Fig 6. The proportion change of regional carbon budget in four regions from 1996 to 2020

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scientific to an increase in overall carbon balance volume the proportions of Western and Central regions decreased from 1996 to 2020.

Through the above analysis and regional comparison, the national carbon budgets deficit shows an upward trend which is fastest in the East, and least in the Northeast.

4 Conclusion and policy suggestion 4.1 Main conclusions The carbon balance analysis of national and regional cultivated land conversion lead to the following conclusions: (1) The area of cultivated land in China is decreasing at a greater rate than the, construction land is increasing. The analysis predicts that the cultivated land area will decrease from 129.33 million ha in 1996 to 120.4 million ha in 2020, whereas the construction land area is predicted to increase from 24.07 million ha in 1996 to 30.73 million ha in 2020.

(2) The rate at which regional cultivated land is predicted to be converted to building land is in the order: East > Central > West >Northeast. The Eastern region is likely to be subjected to a reduction in its carbon sink of cultivated land from 0.79 billion tonnes in 1996 to 0.748 billion tonnes in 2020, as its carbon source is likely to increase from 9.34 billion tonnes to 11.7 billion tonnes in 2020.

(3) The annual carbon budget of cultivated land conversion for China indicates an increasing trend.

Prediction of carbon budgets in 2020, indicate they are largest in the East, with 5.03 billion tonnes, constituting 48% of countrywide carbon budget, second in the West with 2.28 billion tonnes, or 22% of the total. The Central region would be slightly less than the West, with 2.17 billion tonnes, or 20% of the total. The carbon budget of the Northeast is the smallest of 1.08 billion tonnes, or 10% of countrywide total.

Cultivated land conversion is a complex system. The reliability of the estimates of future carbon budgets is restricted by the assumptions made, and the accuracy of the parameters.

The reliability of the predictions declines with increasing future time.

The authorâ&#x20AC;&#x2122;s original intention was to associate the cultivated land conversion with the low carbon economy and apply a dynamic system framework. In this we measured the

quantitative ecological effects of cultivated land conversion on the environment in various areas of China, to raise greater concern from other scholars and the Central government 4.2 Policy proposals Based on the above conclusions, the following policies have been proposed.

(1) In order to reduce net emissions of carbon dioxide in the atmosphere, farmland protection strength should be enhanced; but presently the Eastern region is a key area of cultivated land conversion in China where farmland protection should be more strongly empowered. The area of vegetation should be continuously checked and not further reduced. Full use should be made of farmland, especially grassland and of forest as a carbon sink. While protecting cultivated land, we should increase ecological land area ratio and forest cover by a variety of methods, including the planting a mix of coniferous and broad leaved trees and shrubs,needle leaf mixed forest, shrub and tree to increase net production.

(2) The scale and intensity of building on land should be to strictly controlled.

At present, the rapid development of construction in China will cause increased energy consumption and carbon emissions making the goal of carbon emission reduction difficult to achieve. Therefore, the main tasks are to regulate construction land scale expansion and improve its intensive use.

(3) The role of government is paramount in regulating and developing a low carbon economy and renewable sources of energy. In order to realize sustainable energy utilization of society, we propose that the reduction of emission targets of cultivated land conversion should be taken into the region construction planning of each province, industrial structure should be adjusted continuously, a low carbon industry and renewable energy sources which greatly contribute to GDP should be vigorously encouraged. Moreover, effort should be made to encourage the adoption of a low carbon life style.

Acknowledgements We thank anonymous referees for very helpful comments. And also thank National Natural Sciences Foundation of China (71473249, 51474214, U1361214); National science foundation work special key project (2014FY110800); China Post-doctoral Foundation (2011M500098, 2013T60577); A Project Funded by the Priority Academic Program

Development of Jiangsu Higher Education Institutions (SZBF2011-6B35); Graduate Innovation Project of Jiangsu Province(KYZZ_0389) for the financial support.

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Using climate information to support crop breeding decisions and adaptation in agriculture Dr Pete Falloon1*, Dr Dan Bebber2, Professor John Bryant2, Dr Mike Bushell3, Professor Andrew J Challinor4,5, Professor Suraje Dessai4, Professor Sarah Gurr2, Dr Ann-Kristin Koehler4 1Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, UK, EX1 3PB. 2Biosciences, College of Life and Environmental Sciences, University of Exeter, Stocker Road, Exeter, EX4 4QD, UK. 3Syngenta Limited, European Regional Centre, Priestley Road, Surrey Research Park, Guildford, Surrey, GU2 7YH, UK. 4School of Earth and Environment, University of Leeds, Leeds, UK LS2 9JT. 5CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS), Cali, Columbia 6713, South America *Corresponding author, email: pete.falloon@metoffice.gov.uk Summary Population growth in the next few decades will increase the need for food production, while the yields of major food crops could be impacted by the changing climate and changing threats from pests and pathogens. Crop breeding, both through conventional techniques, and GM assisted breeding could help meet these challenges, if adequately supported by appropriate information on the future climate. We highlight some of the major challenges for crop breeders and growers in the coming decades, and describe the main characteristics of crop breeding techniques and other adaptation options for agriculture. We review recent uses of climate information to support crop breeding decisions and make recommendations for how this might be improved.

We conclude that there is significant potential for breeders to work more closely with climate scientists and crop modellers in order to address the challenges of climate change. It is not yet clear how climate information can best be used. Fruitful areas of investigation include: provision of climate information to identify key target breeding traits and develop improved success criteria (e.g. for heat/drought stress); identification of those conditions under which multiple stress factors (for example, heat stress, mid-season drought stress, flowering drought stress, terminal drought stress) are important in breeding programmes; use of climate information to inform selection of trial sites; identification of the range of environments and locations under which crop trials should be performed (likely to be a wider range of environments than done at present); identification of appropriate duration of trials (likely to be longer than current trials, due to the importance of capturing extreme events); and definition of appropriate methods for incorporating climate information into crop breeding programmes, depending on the specific needs of the breeding programme and the strengths and weaknesses of available approaches.

Better knowledge is needed on climate-related thresholds important to crop breeders, for example on the frequency and severity of extreme climate events relevant to the product profile, or to help provide tailored climate analyses (particularly for extreme events). The uncertainties inherent in climate and impact projections provide a particular challenge for translating climate science into actionable outcomes for agriculture. Further work is needed to explore relevant social and economic assumptions such as the level and distribution of real incomes, changing consumption patterns, health impacts, impacts on markets and trade, and the impact of legislation relating to conservation, the environment and climate change.

Key words Climate change, decision-making, pathogens, pests, diseases, adaptation

Future challenges for crop breeding

T

he grand challenges that we face as humans â&#x20AC;&#x201C; a growing population to feed, land degradation, food price spikes, fluctuating energy costs, extreme weather and emerging

pests and pathogens - are all pertinent to threats to agriculture in the 21st century.

Here, we consider how best to exploit climate information to protect our crops against change, with particular focus on informing plant breeding techniques for a changing

climate.

What might the changing climate and growing world population mean for crop production?

The worldâ&#x20AC;&#x2122;s population is projected to increase from 7.2 billion to 8.4 billion by 2030, with 85% of the future population living in the developing

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scientific world (1). The growing population, and changing demographic trends (e.g. lifestyles, wealth, diets) will increase the demand for natural resources. Over the same time period, the demand for water and energy could increase by 40 and 50% respectively and for food by 35% (2).

These pressures could be further exacerbated by the changing climate, particularly in the longer-term.

The global mean surface temperature change for the near-term (2016–2035), relative to 1986–2005 will likely be in the range of 0.3°C to 0.7°C (3). As well as these average changes, more frequent hot and fewer cold temperature extremes are projected over most land areas on daily and seasonal timescales. As a result,

more frequent and longer heat-waves are projected although there will continue to be occasional cold extremes (3). It is not only the absolute change in temperature which is important for agriculture, but how temperatures change relative to present-day experience of background variability in climate. For example, due to the relatively small inter-annual temperature variability in the tropics (4), low latitude countries are projected to experience measureable change in climate earlier than temperate regions (Fig. 1).

Furthermore, for many tropical countries, local warming outside past variability is either emerging at present, or could emerge in the next few decades.

Fig. 1, from (4). The map shows the global temperature increase (°C) needed for a single location to undergo a statistically significant change in average summer seasonal surface temperature, aggregated on a country level. As noted in the text, the figure illustrates that due to the relatively small inter-annual temperature variability in the tropics, low latitude countries are projected to experience measureable change in climate earlier than temperate regions. For many tropical countries, local warming outside past variability is either emerging at present, or could emerge in the next few decades. The black horizontal line adjacent to the colour bar denotes the committed global average warming if all atmospheric constituents were fixed at year 2000 levels (the warming implied by previous human activity, even if further greenhouse gas emissions were halted). The small panels show the inter-annual summer-season variability during the base period (1900–29) (± 2 standard deviations shaded in grey) and the multi-model mean summer surface temperature (bold red curve) of one arbitrarily chosen grid cell within the specific country. The shading in red indicates the 5% and 95% quantiles across all model realizations. Reproduced with permission from IOP Science, Copyright © 2011 IOP Science.

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Annual average precipitation is likely to increase over high latitudes and many mid-latitude wet regions of the world, but decrease over many midlatitude and subtropical dry regions.

Over most of mid-latitude and wet tropical regions, extreme precipitation events are likely to become more intense and more frequent by the end of this century.

The area encompassed by monsoon systems is likely to increase, but with weaker monsoon winds, more intense precipitation, earlier (or unchanged) onset dates and later retreat dates, resulting in longer monsoon seasons over many regions (3).

How will this affect the major energy food crops of the world? The overall effect will depend strongly on the nature of local climate change, the crop, region, and management factors, and whether climate adaptation measures are used. Here, we consider the crops which currently cover over around 40% of global agricultural land (rice, wheat and maize) and are mindful of the two crops growing in global importance as food and animal feed (soya beans and potatoes). Global wheat harvests, for example – reached 713 million metric tonnes (Mmt) in 2013 (5), with 20% of this being produced within the EU. Yields of the major crops have increased steadily across Europe over the past 40 years (6), largely due to technological developments, although yields have stagnated recently (7,8) The greater rise in historic yields in Northern Europe, compared to Southern Europe (9) is particularly interesting, as it indicates that temperature and rainfall may have more strongly influenced yields in Northern Europe, and warming may already be affecting European yields (Fig. 2).

More broadly, the IPCC Fifth Assessment Report (10) indicates that recent climate trends (particularly warming (11)) have negatively impacted wheat and maize production in many regions, with corresponding reductions in global aggregate production of these crops. Recent trends in climate appear to have had smaller impacts on rice and soya bean yields both in major growing regions and globally. However, crop yields appear to have benefitted from warming in some cooler high latitude regions such as the UK and northeast China (10).

Analysis of national yield statistics for rice, wheat and maize over the last 50 years also tends to indicate increasing

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Fig. 2, from (9) – Observed (FAO 2003) increases in grain yields of wheat for selected countries in Europe. The greater rise in historic yields in Northern Europe, compared to Southern Europe indicates that temperature and rainfall may have more strongly influenced yields in Northern Europe, and warming may already be affecting European yields. Copyright © 2005, The Royal Society, and reproduced under license.

year-to-year variability for many cropcountry combinations (12).

The effects vary considerably across countries, and crops – for wheat, significant declines in yield variability were found for China, India, France and the UK (12). In some cases, changing yield variability was linked to

changing climate variability: wheat in India, maize in Argentina and France, and rice in Vietnam, Thailand, Myanmar and Japan (12).

Projecting forwards, global wheat harvests will likely decline by 6% for each degree C rise in temperature (11), with yields becoming more

Fig. 3, from (13): Median yield changes (%) for a “business-as-usual” climate scenario (RCP8.5) (2070–2099 in comparison to 1980–2010 baseline), including CO2 effects on crops, over all five General Circulation Models (GCMs) x seven Global Gridded Crop Models (GGCMs) (6 GGCMs for rice) for rainfed maize (35 ensemble members), wheat (35 ensemble members), rice (30 ensemble members), and soy (35 ensemble members). Hatching (stippling) indicates areas where more than 70% of the ensemble members agree on the directionality of the impact factor. Grey areas indicate historical areas with little to no yield capacity. The bottom 8 panels show the corresponding yield change patterns over all five GCMs x four GGCMs with nitrogen stress (20 ensemble members from the crop models EPIC, GEPIC, pDSSAT, and PEGASUS; except for rice which has 15) (Left); and 3 GGCMs without nitrogen stress (15 ensemble members from the crop models GAEZ-IMAGE, LPJ-GUESS, and LPJmL). Reproduced from (13) doi:10.1073/pnas.1222463110; with permission from the National Academy of Sciences.

variable both with time and location, although these results do not include the effect of changing rainfall patterns, or elevated carbon dioxide (CO2) concentrations.

For the end of the century (Fig. 3), using Global Gridded Crop Models (GGCMs) driven by data from General Circulation Models (GCMs), Rozensweig et al.(13) (who do consider rainfall and CO2 changes) project increased wheat yields in cooler, northern regions such as Northern Europe, Northern Asia, Northern USA, Canada and Southern South America; and declines in wheat yields for warmer zones (Brazil, Africa, India, Indonesia, Australia) as well as the Eastern USA. Maize yields are projected to decline in most regions, except for Northern Europe and Northern Asia, the most northerly parts of the USA, Canada and Southern South America and Southern Australia, largely consistent with findings based on field trials (14). Rice and soya bean yields could decline in many growing regions, Central Asia and parts of East Asia, Northern USA, and Southern South America (13), although increases are indicated for Southern Australia.

These changes are from crop models which include the impact of nitrogen stress, and provide much more pessimistic outlooks for the yields of most crops than models without nitrogen stress, highlighting the importance of nitrogen supply.

However, there are considerable uncertainties in these results, related to the effects of higher CO2 concentrations, water availabilities and the role of irrigation, and across the different crop models, climate models and future scenarios. Greater yield losses are generally projected for the second half of the century than for the first, and there is greater agreement for end-of-century declines in tropical yield declines than in temperate regions.

Less is known about future changes in year-to-year variation in yields, but increasing variability is considered likely (15). The studies discussed here do not generally include adaptation measures to deal with the changing climate – these are discussed later in the paper.

Changes to the water cycle could also have significant impacts on agricultural production. The global demand for irrigation water for crops is projected to increase in crop models which include the effect of elevated CO2, but change little or decrease in crop models that do not include this effect (16).

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scientific By contrast, hydrological models suggest strong increases in irrigation demand (16). Globally, the area under drought is generally projected to increase, but some regional decreases in the time spent in drought could occur, depending on the drought metric used (17,18).

While increases in the occurrence and severity of river flooding are projected for approximately half of the global land area, decreases are indicated for about a third of the land surface (19), particularly in regions where snowmelt dominates spring river flow peaks.

A range of other climate-related factors could also impact future crop production, including changes in storm patterns, sea level rise (due to thermal expansion, melting of glaciers, ice caps and ice sheets), and increased atmospheric ozone concentrations which can negatively affect crop yields (20).

While such changes in climate, crop yields, and the water cycle are important locally and nationally, they could also have impacts in other regions of the world through their effects on trade and markets. By the 2050s, UK winter wheat yields are generally projected to increase in the absence of heat and drought stress (21), although yield losses are projected for the South, and gains for the North (22); these results are highly sensitive to assumptions regarding management and other factors (e.g. fertilization, sowing dates, CO2 fertilization, etc). On the other hand, 40% of food in the UK is currently imported, and this proportion is rising (23), highlighting the UK’s dependency on food production in other regions.

Fluctuations in the world’s crop trading prices also provide a useful marker of the state of agriculture, and respond the needs of a rising population, changes in supply/demand, and to changes in weather and climate. Relative to a scenario with perfect climate change mitigation (avoidance), 2050s rice prices could increase by between 1820%, maize prices by 32-34% and those for wheat by 23-24% (24).

Similarly, a more recent study indicates that by 2050 average producer prices for coarse grains, oil seeds, wheat, and rice could increase by around 20% (25).

At national to local scales, differing impacts on seasonal climate, variability and extremes are projected. For example, the UK 2009 Climate Projections report (UKCP09; 26)

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indicated hotter drier summers, and warmer wetter winters in the long term, averaged over a 30-year period.

However, the underlying natural variability of weather and climate has resulted in recent individual seasons that do not follow these trends, such as the very cold winter of 2010, the wet summer of 2011 and the cold spring of 2013.

The risk of experiencing extreme seasonal weather conditions could change. By the end of the century, the risk of experiencing very hot summers could increase by up to 20-fold, that of very wet winters could increase by up to 6-fold, and that of very dry summers by up to 8-fold. But in the nearer term, projections suggest a 35% chance of experiencing wet summers (until the 2040s) and a 2030% chance of cold winters (until the 2020s) (27); these probabilities decline later in the century.

As a consequence, the UK is projected to experience a doubling in both area of high-quality horticultural/arable land flooded every 3 years, and changes in irrigation demands of -10 to +80% by the 2050s (21). While UKCP09 suggests increases in heavy rainfall events during the winter, very recent studies using high resolution climate models suggest increases in the intensity of very heavy rainfall events in both summer and winter (28).

Changes to the climate variability and the nature of extreme seasons and variability are particularly important for agriculture.

Extreme weather events can have a range of impacts on agriculture (29,30) such as high temperature impacts on crop stress (particularly around flowering), direct heavy rainfall impacts on crops (lodging, waterlogging), inundation of agricultural land, access to land for agricultural operations, soil, nutrient and contaminant loss, and the impacts of drought on water availability.

For example, while summer 2012 was a remarkably wet year from the climatological perspective, it also reduced wheat yields in the UK by 14% (31).

At the other end of the spectrum, the 2003 heat wave in Europe was the hottest summer on record for hundreds of years, and reduced maize yields in France and Italy by 30-35% (32) as a result of the increased heat and drought stress. The 2012 drought in the USA reduced maize yields by up to 25%, with even bigger impacts on exports (33).

Changing risks from crop pests and diseases

Pests and pathogens of agricultural crops are rapidly evolving and spreading around the world, posing an additional threat to food security alongside the climate and socioeconomic challenges noted in the previous section (34).

Pest and pathogen pressures have existed since the dawn of agriculture.

Archaeological evidence suggests that exportation of crops from their centres of origin may have temporarily reduced pest and disease pressures (35).

Similar challenges still exist in the present-day – for example through breeding resistant crop varieties and developing chemical controls which may eventually need further development to overcome rapidly evolving pests and diseases (36).

This is partly because selective breeding has tended to favour the convenience and productivity of genetically uniform domesticated varieties against the protection provided by high diversity and natural defence mechanisms found in wild crop ancestors (27).

Pests and pathogens comprise a diverse range of species with varying biologies (38).

The term ‘pest’ normally refers to insects and other arthropods that generally feed externally on plant tissues. An important example is the Colorado potato beetle that has swept across Eurasia since its introduction from the USA to France in 1922, and recently has reached the far east of Russia (39). This highly adaptable insect has evolved resistance to pesticides and developed burrowing behaviour to survive cold winter conditions found outside its native range (40). The beetle has not yet established in the UK. Plant parasitic nematodes are also sometimes termed pests.

Many nematodes present a significant threat to crop production because they rapidly evolve resistance to chemical protection and many have very wide host ranges (41,42). For example, the tropical root knot nematodes (genus Meloidogyne) appear to have spread to more temperate climates in recent years, and are very difficult to control. Pathogens are microbes such as bacteria, fungi and fungal-like oomycetes, along with non-cellular viruses and viroids, which invade plant tissues and thus have a close biological relationship with their hosts.

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scientific A recent example of a pathogen invader into the UK is the ash dieback fungus, thought to have been imported on nursery stock from the Netherlands (43).

There are growing numbers of reports on pests and pathogens of major food crops invading new regions. A new syndrome known as maize lethal necrosis (MLN), which is probably spread by an insect vector, was first identified in Kenya in 2011, and appears to have spread to Mozambique, Uganda, Rwanda, Tanzania, and, most recently, in Democratic Republic of Congo (44).

Other new pest and pathogen threats for maize include the spread of Goss’s Wilt to Minnesota (USA) and Manitoba (Canada) from Nebraska and surrounding states, Maize Rough Dwarf Virus in Uganda and Kenya, and Stewart’s Wilt to Argentina.

Virulent new pathogen strains affecting key staple crops are also frequently reported. For example, resistance to strobilurin fungicides has been detected in Septoria Tritici Blotch (STB) of wheat in the UK, Australia and New Zealand.

Weather exerts a strong influence on crop pests and diseases. For example, the first outbreak in several decades of Puccinia graminis f. sp. tritici (cereal stem rust) in Germany was related to warm weather in 2013. As well as affecting crop yields, climate warming over the twentieth century has shifted climate zones away from the equator, which has altered the distribution of plant species to maintain their favored climates (45). Observations appear to show that pests and pathogens may also have shifted their distribution (38).

Bioclimatic mathematical models of species distributions attempt to determine species’ temperature and moisture requirements from known distributions, or by experiment, and then use these preferences to estimate potential future distributions based upon climate projections (46,47,48,49). Although the resulting maps of future suitability for pest activity must be treated with caution because of the uncertainty in both the climatic tolerances of species and the climate projections, they generally suggest that pests and pathogens are likely to move to higher latitudes (Fig.

Fig. 4, from (38); Mean latitudinal shift (km yr-1) for pest taxonomic groups in the Northern Hemisphere for all years (1942-2011), and for 1960 onwards.

Estimates are from linear mixed-effects models of latitude against observation year for centred species-level data. Positive values denote a poleward shift, negative values a shift towards the Equator. Error bars show 95% confidence intervals of the mean. Taxonomic groups are abbreviated, and combined observations (All) included for comparison. Groups are ordered by the mean of the coefficients. Lep. – Lepidoptera; Pro. – Protozoa; Col. – Coleoptera; Hem. – Hemiptera; Fun. – Fungi; Hym. - Hymenoptera; Aca. – Acari; Bac. – Bacteria; Iso. – Isoptera; Oom. – Oomyceta; Dip. – Diptera; All. – All groups combined; Thy. – Thysanoptera; Vir. – Viruses; Nem. - Nematoda (for definitions, see glossary).

4, (38)). More generally, IPCC (10) concluded that the distribution of pests and diseases is likely to change due to future climate change, though low confidence was ascribed to this conclusion.

As an example, the UK lies at a high latitude, and can therefore expect further establishment of invasive exotic pests from the warmer south as the climate changes. Producing a synthetic assessment of likely pest and disease impacts is difficult, not least because of the large range of different species and environments involved. There are several additional challenges in making future projections of pest and pathogen distributions.

First, for groups such as fungi and bacteria, infection of plants is highly dependent upon moisture, particularly the duration of leaf wetness during the growing season (50), and the global water cycle remains difficult to model (51). This is particularly the case at very small spatial scales relevant to pest and pathogen impacts such as an individual field, or even at plant level.

Second, pests and pathogens are able to adapt to new climates. For example, the fungus causing STB of wheat leads to losses of almost 1 billion in Germany, France and the UK alone and accounts for 70% of the EU spend on fungicides (S. Gurr, unpublished data). STB thrives in humid climes, such as the northern EU’s “maritime zone”, but has shown rapid adaptation to temperature variation across its global range (52). It has become the dominant pathogen of wheat in temperate climates, taking over from a closely-related fungus (Parastagonospora nodorum) in recent years (53).

Third, there is a need to model not only the suitable habitat in terms of climate, but also where the host crops are likely to grow, and the likely transmission routes for spread. Crop production is affected not only by changes in climate, but also by socioeconomic factors such as demands for biofuel production. The likely transmission routes can be estimated from trade data (54) but uncertainty in the global distributions of pests and pathogens makes this difficult, particularly in the developing world where monitoring is poor (55).

There may also be other climatic impacts beyond establishment risk. For example, warming can allow increased numbers of generations in the growing season, potentially increasing the opportunity for evolution of resistance to pesticides.

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scientific As an island, the UK has the advantage that borders are somewhat more controlled. For example, the recent import ban on fresh produce from India was driven by repeated interceptions of quarantine pests. In contrast, the entry of ash dieback in nursery stock illustrates the need for improved monitoring for pathogens.

At present, UK plant defence strategy is developing new protective genetics and chemistry, while pests and pathogens evolve countermeasures.

This strategy will come under increasing pressure as the threats from pests and pathogens change, particularly as climate change alters survival rates of more exotic invasive species.

Tools to support global food security: bringing together climate projections, crop models, plant breeding and genetics Genetic Tools to Support Plant Breeding in a changing climate

The first successful plant genetic modification (GM) experiments in 1983 (56,57) met with enthusiastic responses from the plant science and plant breeding communities.

These new tools made it possible to study the effects of individual genes, to transfer single genes into elite crop strains (rather than mixing two genotypes as in classical breeding), to by-pass sterility barriers and to look much more widely in the search for useful genetic traits. However, the commercialization of GM crops in the mid-1990s was met with strong opposition, especially in the UK and in the rest of the EU (58,59). This opposition delayed the uptake of GM crops into agriculture, and led to greater expenses in developing new GM varieties. For example, only one GM crop used for animal feed (maize MON 810) is currently grown commercially in the EU. However, the European Parliament has recently approved new legislation on GM crops which will give governments more power to decide whether to grow GM crops.

Nevertheless, in 2014 GM crops were grown in 28 countries on a total area of 181.5 million hectares (60), and some GM crops are bringing positive benefits to agriculture in poorer countries (58,60,61). But can GM and

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Yield (t/ha)

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Fig. 5, from (66): Yield results from Midwest evaluations under water-deficit conditions. Three different corn hybrids carrying a single transgenic event expressing CspB were evaluated in yield trials across the western dryland market. CspB is a gene which codes for an RNA chaperone, which is a set of commonly occurring protein molecules that bind to RNAs and facilitate their function. The gene was first identified in bacteria subjected to cold stress conditions and further research has demonstrated that CspB helps plants cope with drought stress. Yield results were averaged across locations that experienced water-deficit stress during the late vegetative or grain fill periods of the season. Republished with permission of American Society of Plant Biologists, from Castiglioni et al. (2008) (66); permission conveyed through Copyright Clearance Center, Inc.

associated techniques help in the challenge of feeding a growing population in a changing climate?

Breeding for a Changing Climate

Plant breeding has often been aimed at crop traits that result from the expression and interaction of many genes (‘quantitative trait loci’/QTLs) such as yield, usually for specific agrienvironments, plus a small number of more specific traits such as virusresistance.

Breeding programmes may also have led to the loss of adaptation to environmental stresses. On the other hand, molecular breeding based on GM techniques has focused on individual genes (or ‘stacks’ of two or three individual genes) and the traits they encode, including herbicidetolerance, insect-resistance and virusresistance.

The changing climate presents several different challenges for plant breeding (62).

First, there is the need to enhance tolerance of/adaptation to environmental stresses while maintaining yield as far as is possible.

Second, the existence of alleles associated with tolerance in current

elite strains needs to be assessed.

Third, wider ranges of genetic variation need to be exploited including land-races and traditional varieties, wild relatives and, for direct GM techniques, any relevant genes wherever they occur.

Genetic Tools Genetic modification

GM techniques have come a long way since the 1983 experiments.

Much more is known about the molecular details of the process (e.g.

63). Integration into specific sites in the genome is possible (64).

Sophisticated regulatory and targeting mechanisms mean that gene expression can be controlled in developmental time and organismal/cellular space. This includes the ability to down-regulate genes very precisely using RNAi techniques (e.g. ref 65).

Specific genes have been identified that confer at least a degree of tolerance to particular stresses. For example, a maize (corn) variety, carrying a gene from Bacillus subtilis that encodes an RNA chaperone, performs better under drought stress (Fig. 5, 66) than non-transgenic

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scientific control plants (although not as well as when well watered).

This variety was grown commercially for the first time in 2013. Similarly, a gene from sunflower that encodes a stress-responsive transcription factor confers a degree of drought stresstolerance on soya bean (67, 68), which otherwise, has very high water requirements.

In dry regions such as Southern Europe, future increases in intense rainfall events and increased irrigation demands may cause greater soil salinisation due to greater water loss past the crop root zone (69). The gene TmHKT1;5-A encodes a sodium transporter and confers salt-tolerance on einkorn wheat (Triticum monococcum).

This wheat species will not hybridize with either pasta wheat (T. durum) or bread wheat (T. aestivum). However, via a series of plant breeding procedures that took 15 years, Munns et al., (70) used ‘conventional’ breeding to transfer the gene into T. durum. Wheat plants with the transgene suffered much less effect on yield when exposed to high NaCl (salt) concentrations than did control plants.

It would have been quicker to transfer the gene by GM techniques than by ‘conventional’ breeding, but such a transfer would have been ‘subject to the restrictions associated with genetically engineered material.’

This is an example of how GM techniques could provide more rapid climate adaptation solutions than conventional plant breeding. On the other hand, not all GM-related crop developments will provide direct benefits in a changing climate. For example, it is claimed, albeit by a group opposed to GM technology, that yields of Round-up-Ready soya bean are more affected by water deficit than are yields of ‘conventional’ varieties (71).

determining the distribution of DNA sequence motifs, ranging from RFLPs via micro-satellites to single-nucleotide polymorphisms has brought greater speed and reliability to MAS. MAS is faster because the breeder does not have to wait for the appearance of the marker trait. It is also more reliable because a much greater density of markers can be achieved, and therefore much more closely linked markers than are usually available, if the marker is based on a coding sequence with a visible phenotype.

Genomics

DNA sequencing technology has advanced considerably since its inception in the late 1970s; sequencing a genome is now very much faster and cheaper (73).

High density marker maps can now be assembled not only for elite strains but also for traditional varieties, landraces and even wild relatives. Through genome-wide associative analysis, this also provides a tool to search for as yet undiscovered alleles, especially alleles associated with resilience to environmental stresses (62).

While some newly discovered alleles may be introgressed into appropriate strains and cultivars by conventional breeding, others, especially those located in wild relatives or in unrelated species, will require the use of GM technology. Genetic tools can potentially provide farmers and growers extra capability to produce more, use lesser inputs and avoid negative impacts on the environment, alongside the use of technology to improve input use efficiency and best

practices. It will be difficult to face the perfect storm with an incomplete tool kit (59, 74).

Using climate information in crop breeding programmes Crop varieties are bred for particular environmental conditions.

Genotype by environment interactions (GxE) is an important issue in plant breeding, and given the long timescale of crop breeding cycles (5-20 years), it would be beneficial to understand future environmental changes so that breeding programmes can ensure suitability for both the current and future (time of release) climates.

In general, yield data generated from crop breeding and evaluation programmes arise from a series of field trials known as multi-environment trials (MET) which allow the investigation of varietal performance across a range of environmental conditions.

For example Ober et al. (75) genotyped 135 lines using 11 diagnostic markers plus seven markers based on published QTL data to understand the drought tolerance of a wide range of wheat varieties in the UK. Characterisation of the crop environment using external (e.g. climate) data can help to explain GxE. The crop breeding cycle

Fig. 6 demonstrates a typical crop breeding cycle focused on UK wheat, although in practice this will vary depending on the crop and the techniques being used in breeding.

Marker-assisted selection

Marker-assisted selection (MAS) has been part of the plant breeder’s tool kit for over 60 years.

MAS involves identifying an easy-tofind genetic marker that is closely linked to a wanted allele that encodes or regulates a desirable trait. At first, the loci used as informative markers were often involved in regulating or encoding obvious external phenotypic characters.

For example, in 1935 Rasmusson used pea flower colour as a linked marker for a flowering time QTL (72).

However, the advent of techniques for

Fig. 6: A typical crop breeding cycle, based on UK winter wheat.

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scientific The timescales shown represent a reasonable timeline given infrastructure to fast-track the process of backcrossing, while the fastest timelines are achievable with Doubled Haploids, which are not yet feasible with all crops.

Climate information and climate conditions will have different implications in different stages of the breeding process.

The generic stage plan shown here (Fig. 6) would be used across a group of projects in a crop breeding team to help visualize progress in the series of product candidates. A crucial starting point is a clear definition of success criteria in the product profile at the outset. When creating a product profile crop breeders aim to address current concerns and requirements from farmers while balancing this against perceived future needs.

In the UK, these requirements will include the agronomic properties measured by the Home Grown Cereals Association (HGCA) such as yield and disease resistance, plus the processing industry requirements, and also the risk associated with weather extremes, particularly drought and heat stress.

For example, climate change will be one factor that could increase the importance of abiotic stress as a value adding trait.

The product profile could be to increase yield under drought or heat stress (relative to the controls) without any negative impact on yield under normal conditions. A critical activity would therefore be to identify genes associated with the drought or heat stress trait, and bring them into the breeding programme.

Once a product profile is developed, the number of candidates is progressively reduced through single location, and later multi-location trails representing the main growing areas in the UK. The varieties launched will also need to be tailored to the local conditions. The multi-location trials therefore implicitly include local climate as well as performance on the predominant soil types.

The National List (NL) trials represent the scale up in availability of seed to extend the scope of the trials to different parameters and multiple locations. The NL trails are designed to produce enough data for the new variety to be recognized by partners and important customer groups (e.g. for wheat: National Institute of Agricultural Botany, millers, brewers and bakers). Since wheat is used in a range of processing industries, it needs

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to have the right properties for specific applications. The Recommended List (RL) trials then aim for inclusion on the HGCA recommended list, which is almost essential for commercial success. Farm advisors and farmers will select their varieties based on their own experience and the data comparisons offered on the RL.

Integrating environmental data into breeding programmes There are currently various approaches for integrating environmental data into breeding programmes, to integrate both current and future conditions.

The two most common approaches are a) using climatological and other environmental data together with yield trial data for subdividing crop growing areas into Mega Environments (MEs) and b) using crop simulation models (to incorporate changing conditions) in defining a location and to characterise the target population of environments (TPE). This is a mixture of environments expected for the intended region of production (76). a) Using climatological, environmental and yield data to define Mega Environments An example is the Mega Environments (MEs) from the International Maize and Wheat Improvement Center (CIMMYT), where ~800 testing sites were geo-referenced and classified according to predominant ME by wheat (77) and maize (78) scientists.

Then underlying climatic and edaphic factors (soil characteristics) were extracted and used to determine quantitative criteria for mapping the MEs. A limitation is the static definition of MEs (77). They do not include a temporal aspect to define locations or regions in terms of probability or frequency of occurrence of different environment types. Some locations may fluctuate between multiple MEs depending on seasonal conditions (79).

Ortiz et al., (80) looked at wheat ME zonation in the Indo-Gangetic Plains (IGPs) and how the zonation may change by 2050 as a result of possible climatic shifts. The study was based on a doubling of CO2 using data from the CCM3 climate model downscaled to high resolution. They found that the currently favourable wheat growing area which accounts for 15% of global wheat production might be reduced by as much as 51% by 2050 owing to early and late season heat stress. On

the contrary the cool temperate wheat-cropping systems might shift northwards from 55째N to 65째N. b) Using modelling tools to define Target Population of Environments (TPE) Using modelling tools offers the opportunity to characterize the TPE and to consider possible changes in the frequency of different stress patterns in current and future conditions. The approach is powerful if yield is limited by a few major stresses only. Chapman et al. (81) used a crop simulation model with long-term weather records to determine seasonal sequences of drought stress for sorghum in Australia and grouped them into environmental types with specific stress patterns. They found that the frequency of sorghum drought stress patterns within a normal 2-year MET often did not match that of the complete set of environments due to inter-annual climate variation.

As a result, the genotype average from a 2-year MET can be a poor estimate of the long-term performance of a genotype. The estimate can be improved if the data were weighted according to their representativeness of the MET in the TPE (81) and therefore the revised estimate can assist the interpretation of the GxE for yield (82).

Informing current and future breeding requirements Forecasting information for the midterm (e.g. 20 years) by predicting the main climatic onditions, potential pest and disease pressure and growing season length, can help breeders to esign experiments to focus on genetic variability in the environments of interest. Additionally, such information could potentially help in the choice of trial locations, and in determining whether they cover the climate of the main growing regions. Some studies have taken the approach further to study breeding-simulation scenarios which link phenotypic consquences to changes in genetics via stable associations with crop model parameters.

These are studies to explore GxE interactions for complex traits.

Examples of this approach are given by (83) for drought stress of maize in Australia, (81) for drought stress in sorghum in Australia,(84) for drought stress of wheat in Europe and (85) of maize in Europe. Studies such as thee have been used to infer priorities for crop breeding in a changing climate.

For instance Semenov (86) and Semenov and Shewry (87) propose focusing on wheat varieties

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scientific

Number of hot days per summer

Fig. 7, from (88). Uncertainties in the number of summer days in 2030â&#x20AC;&#x201C;2059 ith maximum temperature > 30 °C, derived from different sources. (Left) Uncertainty de to future scenarios = difference between the raw output of the Institut Pierre Simon Laplace IPSL) Atmosphere-Ocean General Circulation Model (AOGCM) climate model from to different future emissions scenarios (Special Report on Emissions Scenarios (SRES) A1B (assuming a globalised world with rapid economic growth, and a balanced emphasis on all energy sources) and A2 (assuming a world with regionally oriented economic development). (Middle) Uncertainty due to model calibration and errors = difference between the mean bias-corrected (BC) and calibrated (CF) projections using the Quantifying Uncertainties in Model Predictions (QUMP) ensemble to predict the IPSL AOGCM data. (Right) Uncertainty due to climate model responses = 2 x the standard deviation in the BC calibrated QUMP ensemble, predicting the IPSL AOGCM data.

Reprinted from Agricultural and Forest Meteorology, 170, Hawkins E., Osborne T.M., Ho, C.K., and Challinor A.J., Calibration and bias correction of climate projections for crop modelling: An idealised case study over Europe, 19-31, Copyright (2013), with permission from Elsevier.

tolerant to high temperature rather than to drought. Yet, if the supporting studies investigate only a small range of future environmental stresses they may be inadequate. In addition, it is important to understand the different sources of uncertainties inherent in future crop impact assessents, particularly those related to high temperature events and model calibration (Fig. 7; 88) Zheng et al. (89) and Harrison et al.

(85) demonstrate how the same approach can be used to inform breeding for changing future conditions. Zheng et al. (89) analyzed patterns of frost and heat events across the Australian wheat belt based on 50 years of historical weather station records and eight future scenarios, simulating three contrasting maturity wheat varieties.

They found that by 2050 both last frost and first heat events occur earlier in the season and shift the target sowing and flowering windows by up to 2 and 1 month(s). They conclude that early sowing and longer season varieties would be the best strategies to adapt to future climates and point out that the 5-20 year process of breeding for new varieties needs to accelerate to adapt to future climate.

In turn, this indicates that skilful decadal climate forecasts (e.g. 90) would be of significant value to crop breeders.

Harrison et al. (85) characterised the typology and frequency of drought stress for maize in Europe and their associated yield distributions, and simulated the influence of three different breeding traits on yield under current and future climates. They found that the same four drought stress patterns experienced today will be the most dominant ones in 2050; only their frequencies will change.

Traits having a positive effect on yield for one drought stress pattern do not necessarily have positive effects on a different drought stress pattern. This shows that drought stress patterns which do not change in the future have important implications for breeding. Otherwise breeding would need to be conducted under controlled-stress environments in order to account for the change in drought stress patterns.

Note that here, drought stress patterns are defined as water limitations to the crop during different developmental stages and of different severity, using a simulated water stress index and grouping simulations into

categories (e.g. later water stress, early water stress, no water stress). Although generic assessments of future changes in hydrological and meteorological drought occurrence are available (e.g.

17,18), these are rarely tailored to agricultural applications or cropspecific impacts, and do not directly relate to crop drought stress patterns.

Approaches based on crop models can only study a limited number of genotypes, because a large number of parameters need to be measured in order to determine model parameters for each genotype. Another approach is to use crop modelling as a tool to perform a physiological integration of environmental data in order to derive stress covariates (environmental variables such as temperature which can be used to predict crop responses like heat stress; 91-94). The approach has good potential if yield cannot be explained by a few major stresses as above (94). In order to include weather data in an interpretable way, reduce the number of variables, and accommodate non-linear responses.

Stress covariates can be defined by crop development stage (91-94). A crop model is then used to determine crop phenology â&#x20AC;&#x201C; ideally for each genotype in each environment. As this is often not feasible, it is common to divide MET data into sets of maturity types and to calibrate a crop model to common varieties that fall into those maturity types (94-96). This approach makes the assumption that the stress response genetic architecture is the same among genotypes at a given developmental stage. Heslot et al. (94) explicitly modelled whole-genome markers and their differential response to the environment using stress covariates to understand the genetic architecture of GxE for winter wheat in France. A clear picture emerged about the stresses creating the most GxE, but no correlation between the importance of the marker and their main effect on yield could be found. As none of the considered multiple stresses was suspected to be the main cause of GxE, it was not meaningful to directly use the stress covariates to group environments as has been done in previous studies (81,97,98). The alternative approach is to group environments using the predicted GxE response, which is of direct interest for crop breeding because it allows grouping on the level of genetic correlation between environments.

However, a large and broad sample of environments types is needed for this method to be effective.

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scientific Bridging the gap – Other climate adaptation measures It is clear that the crops of the future will be growing under changing climatic conditions.

Crop breeding and GM techniques can potentially help avoid some of the negative impacts and make the most of potential positive effects of climate change. However, for future food security, a broader range of other adaptation measures, such as changes in agricultural technology and management techniques are also required.

There are two broad approaches for evaluating climate adaptation options (99): ‘top-down’ and ‘bottom-up’. The top-down approach starts with the question: “how will future climate change?” (100), and is driven by information from climate models, future scenarios and the resulting impacts; the final stage is to design and assess “on the ground” adaptation options. The bottom-up approach begins with the decision-making context (101-103), starting by

identifying vulnerabilities, sensitivities, and thresholds to proposed adaptation measures. These measures and their timing are then assessed against present and future drivers, including climate. These two approaches have different merits (104) – for instance top-down approaches are appropriate when uncertainties are shallow, while bottom-up approaches are more applicable when uncertainties are deep. Bottom-up approaches also need information providers to work closely with decision makers (105) to understand their needs which can be very effective, but often need to be tailored to each decision context (106,107).

An important component of making robust assessments relevant to adaptation (108,109) is a comprehensive understanding of the uncertainties. Scientists and decisionmakers may interpret uncertainty ranges differently, potentially resulting in poor decision making (104). The appropriate treatment of uncertainty ranges may also vary according to the nature of adaptation required. In agriculture, adaptation could mean:

Fig. 8, from (15): Quantification of the benefits of adaptation. Percentage yield change as a function of temperature (a) and precipitation (b), for the 33 paired adaptation studies, across all regions and crops (wheat, rice and maize). Shaded bands indicate the 95% confidence interval of regressions consistent with the data based on 500 bootstrap* samples, with blue and orange bands corresponding to with and without adaptation. c,d, The difference between simulations with and without adaptation for temperature (c) and precipitation (d) are shown, using the same bootstrapping technique. Note that part of the lack of decline at high temperatures in the non-adaptation curve in (a) is due to high representation of rice (23 of 28 no-adaptation studies with T >4°C and yield change >0), which shows less sensitivity to high local temperature change than other crops. The dataset used is considered reasonably representative of the major global producers, although Brazilian maize and rice in Indonesia and Bangladesh are under-represented. Reprinted by permission from Macmillan Publishers Ltd: Nature Climate Change (doi:10.1038/nclimate2153), copyright (2014). *Bootstrapping is any statistical test or metric that relies on random sampling with replacement, allowing measures of accuracy and precision (e.g. bias, variance, confidence intervals, prediction error) to be assigned to sample estimates.

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coping (altering planting dates or crop varieties), adjusting (new crops), or transforming (new production systems, livelihoods, migration). Top-down approaches are important for transformative strategies (110), while bottom-up approaches are useful for incremental (coping) strategies.

Different kinds of climate information are also required for these different strategies – for instance, shorter-term information such as seasonal forecasts are particularly relevant to coping strategies, while longer-term multidecadal climate projections are necessary for assessing transformative strategies (110). Olesen and Bindi (111) also note the need for both short-term and long-term structural and policy changes to support agricultural adaptation.

Recent global-scale analyses suggest there could be considerable positive benefits of adaptation measures – for example, adaptation could result in future yields of major crops that are higher than their no-adaptation counterparts by the equivalent of 7–15% of current yields, with greater benefits for wheat compared to maize (Fig. 8, (15)).

There are also examples of presentday adaptation to changing climate.

Liu et al. (112) found that maize yields in Northeast China had increased between 1981 and 2007, under changes in climate, cultivar selection and crop management. Earlier sowing and the introduction of cultivars with higher thermal time requirements appear to have overcome the negative effects of the changing climate, turning a potential yield loss into a significant increase. However, it is unclear when farmers decided to plant their crops earlier, and whether this was influenced by weather-related factors.

Extreme weather events and climate variability can be particularly challenging for agriculture, as farmers can often adapt to more gradual changes, but managing the impacts of extreme events is much more difficult.

This is particularly the case where opposite extreme weather events occur in close succession.

In the UK, for example, March 2012 was the fifth driest and third warmest March on record, followed by the wettest April on record in the UK. It is also difficult to allow for the impacts of extreme weather events in crop breeding programmes because the focus tends to be on maximum yield, and stability of yield rather than for unusual conditions.

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scientific Early warning systems and seasonal forecasts which incorporate extreme events and their impacts on crops could therefore be beneficial tools to support coping strategies (113).

For example, given appropriate prior information on the probability of a drought or heat stress event on the coming season, farmers could plant more resistant varieties which would yield less under normal conditions.

Seasonal climate information could also help farmers to plan ahead more generally, supporting decisions on crop type, variety and to plan field operations (e.g. cultivation to reduce soil damage, fertilizer application to minimize leaching or emission-related losses). Agrochemical companies could also potentially benefit from seasonal to annual weather forecasts linked to pest, weed and disease pressure to plan and manufacture appropriate amounts of products ahead of the season.

Seasonal climate forecasts have found fairly widespread user applications in some regions of the World, notably Africa, the USA and Australia (114,115), but there has been relatively little uptake in Europe (114).

This is partly driven by the relatively limited skill of seasonal climate forecasts in Europe (116-118); if farmers were to rely on these forecasts to make planting choices, these could result in poor results with negative economic impacts. Rather than technical aspects (e.g. accuracy, lead time, and spatial/temporal scale, 119) potential economic and environmental benefits may be the dominant drivers of user uptake of seasonal forecasts. An additional challenge is that seasonal predictions are commonly uncertain, which brings additional challenges in communicating forecast information to end-users (113).

A range of practical adaptation options is available to farmers. In the UK, for instance (21) these could include: n improved soil and water management (residue management, water harvesting, on-farm storage), n improved irrigation techniques, n changing crop types, varieties, and sowing dates, and, n other measures such as planting trees to provide shading and windbreaks.

Summary and future directions The growing population and changing climate are likely to put increasing

pressure on agriculture to produce more food on less land, with lower inputs and lower impacts on the environment. The changing climate could directly affect yields of major food crops, with the impact varying, depending on the crop and region.

There will be changing patterns of threats from pests and pathogens.

There are several tools which could help in meeting this challenge, including conventional breeding, and GM assisted breeding, significantly accelerating the breeding process.

Climate information could potentially inform more focused development and application of these tools in a number of ways.

While current crop breeding programmes do not always explicitly incorporate current or future climate information, doing so could potentially deliver more resilient varieties through a better understanding of sensitivity to present climate, and greater future resilience. Examples of traits which could directly link to climate-related information include resistance to heat and drought stress. However, breeding may also need to account for a wider range of factors (and their combinations/interactions) such as water-logging/flooding, changing pest and disease pressures, changes in atmospheric chemistry (e.g. ozone, carbon dioxide), and potentially salt tolerance. Climate information relevant to these traits (e.g. present and future climatologies) could provide a key input in the first step of a new crop breeding programme: defining the success criteria, or breeding target.

The later phases of a crop breeding programme typically involve testing crop performance across a range of different sites. Weather and climate information could potentially benefit these stages of the breeding process in several ways. Weather data collected at trial sites could be analysed alongside crop performance statistics â&#x20AC;&#x201C; for example to assess which weather extremes were experienced and inform subsequent selection of varieties. The choice of trials sites could be informed through use of tailored present-day and future climatologies, and skillful decadal forecasts would be particularly valuable in this respect.

This emphasizes the importance of defining appropriate, user-focused measures of reliability for such forecasts, and the need for clear information on their current and anticipated future reliability. Testing crop suitability for future climates may also require trialing crops in a wider

range of climate zones than is done at present, or making use of controlled environments, whilst recognizing that a range of other factors will differ across sites (e.g. soil type, pest and disease pressures).

Appropriate methods should be chosen for incorporating climate information into crop breeding programmes, depending on both the specific needs of the breeding programme, and on the strengths and weaknesses of available approaches.

For example: n Combining climate, environmental and yield data to define MEs provide useful coarse-scale information, but this has a number of limitations, such as lack of both a temporal aspect (e.g. incorporating future climate changes) to define locations, and of probabilities of different types of environment). n The use of modelling tools to define TPEs is only recommended where yield is limited by a few major stresses. n Using crop models to integrate environmental data and derive stress covariates may be a better choice where yield is limited by many stresses.

Alongside crop breeding techniques, a range of practical adaptation options is also available to the farmer, providing complementary ways to cope with the changing climate; adopting well chosen adaptation measures could potentially provide significant benefits. Appropriate approaches for assessing and evaluating adaptation options will lead to more robust decision making. In the context of this paper, bottom-up approaches could be particularly important for designing near-term (coping) adaptation options in agriculture; a blend of top-down and bottom up approaches may be suited to informing the â&#x20AC;&#x2DC;adjustingâ&#x20AC;&#x2122; nature of crop breeding programmes. Seasonal forecasts, and related early warning systems tailored to agricultural applications could provide significant benefits in near-term adaptation.

There are, however, several challenges in delivering such early warning systems, such as variable forecast reliability depending on the region of the world, and effective communication of probabilistic forecasts to users. In addition, integrated provision of agriculturerelevant weather and climate forecast information across timescales (e.g. days to decades) could support better decision-making, rather than provision through separate channels/sources as is mainly done at present.

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scientific In order for climate information to be usable in agriculture, it needs to be provided at the right time, in the right format, and to be actionable (120).

In turn, to be actionable (121), climate information must be salient (perceived relevance), credible (perceived technical quality) and legitimate (perceived objectivity of the process by which the information is shared). In the context of this article, it is particularly important to gain a better understanding of the thresholds of importance to users. For instance, the outcomes of weather and climate science could be better tailored towards crop breeding programmes using knowledge of the frequency, occurrence and severity of extreme climate events that could be included in the product profile.

More relevant information on the impacts of changes in climate variability and extremes would therefore be particularly beneficial.

Extreme climate event analyses may also need to be better tailored to crop breeding programmes (e.g. expressed in terms of impact on crop growth and development, rather than as rainfall or soil moisture deficits). An important issue here is that of spatial scale: understanding the spatial scales at which robust climate information can be provided, and how this relates to the current scales of breeding programmes (e.g. MEs, TPEs).

A particular challenge for translating climate science into actionable outcomes for agriculture is the effective communication of the uncertainties (114) , which are an inherent part of climate and impact projections.

Figure 9 (from 109) illustrates how the information from uncertain climate impact projections could potentially be enhanced – for example, using “detectable”, or predictable signals in crop yields where they exceed the total

Figure 9 (from 109): Uncertainty* in decadal mean wheat yield in China, from different sources (Upper): climate model ensemble (Quantifying Uncertainty in Model Predictions (QUMP), blue), crop model ensemble (General Large-area Model for Annual crops (GLAM), green), and natural variability in decadal mean yield (orange). The total uncertainty (solid black) and actual change in decadal mean yield normalized** to the baseline (signal, dashed black) are also shown.

The signal (or rate of change) in decadal crop yields is detectable when it exceeds the total uncertainty. (Lower) Fraction of total variance*** explained by the three separate components of uncertainty. These metrics are shown assuming no adaptation (Left) and temperature adaptation. Reproduced from Vermeulen, SJ, Challinor, AJ, Thornton, PK, Campbell, BM, Eriyagama, N, Vervoort, JM, Kinyangi, J, Jarvis, A, Läderach, P, Ramirez-Villegas, J, Nicklin, KJ, Hawkins, E and Smith, DR (2013) Addressing uncertainty in adaptation planning for agriculture. Proceedings of the National Academy of Sciences of USA, 110 (21). 8357 – 8362; with permission from the National Academy of Sciences. *Uncertainty here is presented as percentage of the actual change in yield **Change in yield expressed as % change relative to the baseline (present-day) value ***Expressed as a percentage of the total variance (or uncertainty) in yield change.

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uncertainty.

Furthermore, Figure 9 demonstrates that decadal mean yields are predictable for a more heat-tolerant crop (with adaptation), but not for a crop that is susceptible to heat stress (without adaptation).

This is because increases in extreme heat stress increase the interannual variability of yield, and therefore lead to greater overall uncertainty for nonheat tolerant crop yields Note that in this case, adaptation lowers the total uncertainty (solid black line) in future yield changes, although the actual yield change (dashed black line) is actually greater with temperature adaptation.

Several more specific future research needs are outlined below. Firstly, to deliver GM techniques (and conventional breeding outcomes) in support of food security in a changing climate, a more detailed understanding of the patterns and timing of biochemical and physiological responses to stresses across different crop types is needed (122). In turn, this requires an understanding of the relative importance of individual components of the response pathways (123) and the genes involved in their regulation.

Better information is needed on the networks of regulatory elements associated with a range of environmental stresses (such as those currently under investigation in Arabidopsis (124)). Secondly, changes in future pest and disease pressures are particularly uncertain.

Management methods which recognize the ecological and evolutionary principles behind pest and pathogen migration and evolution are therefore also needed to ensure future food security.

There is significant potential for strong collaboration between different scientific disciplines (e.g. climate science, crop science, pathogen biology, and plant genetics) and the agricultural industry (e.g. both growers and breeders) to deliver more resilient solutions for future food security in a changing climate.

More broadly, because of the wideranging implications of crop breeding for a changing climate, further work should explore relevant social and economic assumptions such as the level and distribution of real incomes, changing consumption patterns, health impacts, impacts on markets and trade, and the impact of legislation relating to conservation, the environment and climate change.

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scientific Acknowledgements This article was supported by the Joint DECC/Defra Met Office Hadley Centre Climate programme (GA01101) and the EUPORIAS project, funded by the European Commission 7th Framework Programme for Research, grant agreement 308291

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List of abbreviations A2 – A2 emissions scenario.

AOGCM – atmosphere only general circulation model BC – bias-corrected projections.

CCM3 – Community Climate Model version 3 (of National Centre for Atmospheric Research) CCRA – (UK) Climate change risk assessment CF – calibrated projections.

CIMMYT – International Maize and Wheat Improvement Center CspB – a gene which codes for an RNA chaperone, which are commonly occurring protein molecules that bind to RNAs and facilitate their function.

The gene was first identified in bacteria subjected to cold stress conditions and further research has demonstrated that cspB helps plants cope with drought stress.

Defra – Department for Environment, Food and Rural Affairs (United Kingdom) DNA – Deoxyribonucleic acid FAOSTAT – Food and Agriculture Organization statistics database GM – genetically modified GxE – Genotype by environment HGCA – (UK) Home Grown Cereals Association IGP – Indo-Gangetic Plains IPCC – Intergovernmental Panel on Climate Change IPSL - Institut Pierre-Simon Laplace MAS – marker-assisted selection MET – multi-environment trials MLN – Maize lethal necrosis NaCl – sodium chloride NIAB – National Institute of Agricultural Botany NIC – National Information Center (USA) NL – national list (trials) QTL – quantitative trait loci QUMP – Quantifying uncertainty in model predictions RL – recommended list (trials)

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scientific RLFP – Restriction fragment length polymorphism RNA – Ribonucleic acid RNAi – Ribonucleic acid interference SRES – IPCC special report on emissions scenarios STB – Septoria tritici blotch TPE – target population of environments UKCP09 – 2009 United Kingdom Climate Projections UN – United Nations

Glossary of terms Acari – a taxon of arachnids that contains mites and ticks.

Alleles – one of a number of alternative forms of the same gene or same genetic locus. Sometimes, different alleles can result in different observable phenotypic traits, such as different pigmentation.

Arabidopsis. – (rockcress) is a genus in the family Brassicaceae. They are small flowering plants related to cabbage and mustard. This genus is of great interest since it contains thale cress (Arabidopsis thaliana), one of the model organisms used for studying plant biology and the first plant to have its entire genome sequenced.

Changes in thale cress are easily observed, making it a very useful model.

Backcrossing – Backcrossing is a crossing of a hybrid with one of its parents, or an individual, genetically similar to its parent, in order to achieve offspring with a genetic identity which is closer to that of the parent.

Bacteria – a large domain of prokaryotic (single celled) microorganisms.

Bioclimatic mathematical model – a model for predicting suitable plant habitat to biological and climatic factors.

Bottom up – In the bottom-up approach, climate adaptation is framed as a social and institutional process that involves many actors and many decisions at different levels. Outcomes of actions can usually not be predicted because they depend on actions and interactions of many actor groups as well as the social and cultural context.

Chaperone – In molecular biology, molecular chaperones are proteins that assist the covalent folding or unfolding and the assembly or disassembly of other macromolecular structures.

Climate adaptation – a response to climate change that seeks to reduce the vulnerability of social and biological systems to current climate change and thus offset its effects.

Climate model – Climate models use

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quantitative methods to simulate the interactions of the atmosphere, oceans, land surface, and ice. They are used for a variety of purposes from study of the dynamics of the climate system to projections of future climate.

Coleoptera – biological classification for the beetle group of insects.

Confidence interval – in statistics, this gives an estimated range of values which is likely to include an unknown population parameter, the estimated range being calculated from a given set of sample data.

Crop model – a simulation model that helps estimate crop yield as a function of weather conditions, soil conditions, and choice of crop management practices.

Crop phenology – the study of periodic crop plant life cycle events (such as emergence of leaves and flowers) and how these are influenced by seasonal and interannual variations in climate, as well as habitat factors (such as elevation).

Decadal forecast – Ten year climate forecasts, also called 'near-term' climate predictions, range up to a decade ahead. Predictions account for natural variability and climate change as these are expected to be of similar size in many parts of the world over this forecast period. Forecasts are experimental, so at this early stage of development expert advice is needed to assess the reliability of regional predictions.

Developmental stage – crop development defined using the 10 stages of Zadok’s scale, from germination to ripening.

Diptera – the biological order of twofunctional winged insects including flies.

DNA sequence motifs – Sequence motifs are short, recurring patterns in DNA that are presumed to have a biological function.

DNA sequencing – the process of determining the precise order of nucleotides (organic molecules that serve as the monomers, or subunits, of nucleic acids like DNA and RNA) within a DNA molecule.

Doubled haploid – a doubled haploid plant has cells containing 2 gene sets which are exactly identical.

Drought metric – a metric used to describe the severity of drought, usually calculated from weather variables such as precipitation, although a variety of drought metrics exist including meteorological, hydrological and agricultural.

Drought stress patterns – water limitations to the crop during different

developmental stages and of different severity, using a simulated water stress index and grouping simulations into categories (e.g. later water stress, early water stress, no water stress) Edaphic factors – factors related to soil, such as drainage, texture, or chemical properties such as pH, and their relationships with plant communities.

Ensemble – In physics, a statistical ensemble is a large set of copies of a system, considered all at once; each copy of the system representing a different possible detailed realisation of the system, consistent with the system’s observed macroscopic properties. A climate ensemble involves slightly different models of the climate system.

Forecast skill – a scaled representation of forecast error that relates the forecast accuracy of a particular forecast model to some reference model. For example, a perfect forecast results in a forecast skill of 1.0, a forecast with similar skill to the reference forecast would have a skill of 0.0, and a forecast which is less skillful than the reference forecast would have negative skill values.

Fungi – any member of a large group of eukaryotic organisms that includes microorganisms such as yeasts and molds, as well as the more familiar mushrooms.

Future scenario – Because it is difficult to project far-off future emissions and other human factors that influence climate, scientists use a range of scenarios using various assumptions about future economic, social, technological, and environmental conditions.

GCM – general circulation model Genes – A gene is the basic physical and functional unit of heredity. Genes, which are made up of DNA, act as instructions to make molecules called proteins.

Genetic architecture – Genetic architecture refers to the underlying genetic basis of a phenotypic trait (itself the composite of an organism's observable characteristics or traits, such as its morphology, development, biochemical or physiological properties, phenology, behaviour, and products of behaviour).

Genetic correlation – Genetic correlation is the proportion of variance that two traits share due to genetic causes.

Genome – an organism's complete set of DNA, including all of its genes. Each genome contains all of the information needed to build and maintain that

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scientific organism.

Genome-wide Associative analysis – In genetic epidemiology, a genomewide association study (GWA study, or GWAS), also known as whole genome association study (WGA study, or WGAS) or common-variant association study (CVAS), is an examination of many common genetic variants in different individuals to see if any variant is associated with a trait.

Genotype – the genetic makeup of a cell, an organism, or an individual usually with reference to a specific characteristic under consideration.

GGCM – global gridded crop model Goss’s wilt – a disease of maize which is caused by a bacterial pathogen that overwinters in residue of corn and several grasses.

Hemiptera – Hemiptera is an order of insects most often known as the true bugs, comprising around 50,000–80,000 species of cicadas, aphids, planthoppers, leafhoppers, shield bugs, and others.

High resolution climate models – climate model with a high spatial resolution (grid box size). High resolution is often necessary to realistically capture climate extremes, particularly for precipitation.

Hymenoptera – one of the largest orders of insects, comprising the sawflies, wasps, bees and ants. Over 150,000 species are recognized, with many more remaining to be described.

Introgressed – Introgression, also known as introgressive hybridization, in genetics (particularly plant genetics) is the movement of a gene (gene flow) from one species into the gene pool of another by the repeated backcrossing of an interspecific hybrid with one of its parent species.

Isoptera – or termites are small to medium sized insects ranging form 320 millimetres in body length.

Lepidoptera – a large order of insects that includes moths and butterflies Loci – In genetics, a locus (plural loci) is the specific location of a gene, DNA sequence, or position on a chromosome.

Marker map – A genetic marker is a gene or DNA sequence with a known location on a chromosome that can be used to identify individuals or species.

Mapping is putting markers in order, indicating the relative genetic distances between them, and assigning them to their linkage groups on the basis of the recombination values from all their pairwise combinations.

Median – In statistics and probability theory, the median is the numerical value separating the higher half of a

data sample, a population, or a probability distribution, from the lower half Microsatellites – also known as simple sequence repeats (SSRs) or short tandem repeats (STRs), are repeating sequences of 2-5 base pairs of DNA. Nematoda – see nematode Nematode – The nematodes or roundworms constitute the phylum Nematoda. They are a diverse animal phylum inhabiting a very broad range of environments. While most of the thousands of species of nematodes on Earth are not harmful, some nematodes parasitize and cause diseases in humans and other animals.

Oomyceta – see oomycetes Oomycetes – The Oomycota include the so-called water molds and downy mildews, and absorb their food from the surrounding water or soil, or may invade the body of another organism to feed. As such, oomycetes play an important role in the decomposition and recycling of decaying matter.

Other parasitic species have caused much human suffering through destruction of crops and fish.

Pathogen – a biological agent that causes disease or illness to its host.

Precipitation – any form of water – liquid or solid – falling from the sky. It includes rain, sleet, snow, hail and drizzle plus a few less common occurrences such as ice pellets, diamond dust and freezing rain.

Probabilistic projection – projections of future climate that assign a probability level to different climate outcomes.

Product profile – derived from the needs of growers, distributors and consumers of crops, The product profile serves as a guideline for product development and is the basis for variety profiles.

Projection – The term “projection” is used in two senses in the climate change literature. In general usage, a projection can be regarded as any description of the future and the pathway leading to it. However, a more specific interpretation has been attached to the term “climate projection” by the IPCC when referring to model-derived estimates of future climate.

Protozoa – single cell organisms.

Quantiles – values which divide a statistical frequency distribution such that there is a given proportion of observations below the quantile.

Regression – In statistics, regression analysis is a statistical process for estimating the relationships among variables.

Seasonal forecast – Weather forecasts provide information about the weather expected over the next few days.

While it is generally not possible to predict these day-to-day changes in detail beyond about a week ahead, it is possible to say something about likely conditions averaged over the next few months. Seasonal forecasts provide information about these long-term averages.

Single nucleotide polymorphism – or SNP, is a variation at a single position in a DNA sequence among individuals.

Soil salinisation – the accumulation of soluble salts of sodium, magnesium and calcium in soil to the extent that soil fertility is severely reduced.

Stewart’s wilt – a serious bacterial disease of corn caused by the bacterium Pantoea stewartii. This bacterium affects plants, particularly types of maize or corn such as sweet, flint, dent, flower and popcorn.

Stress covariate – In statistics, a covariate is a variable that is possibly predictive of the outcome under study.

A covariate may be of direct interest or it may be a confounding or interacting variable. For example, for crop heat stress, different weather variables could be stress covariates.

Surface temperature – the Earth’s average above-ground and sea surface temperature.

Taxonomic group – animal or plant group having natural relations Thermal time requirements – time and temperature requirements for various crop development processes to occur, such as germination, emergence and seedling development Thysanoptera – or Thrips, are tiny, slender insects with fringed wings.

Other common names for thrips include thunderflies, thunderbugs, storm flies, thunderblights, storm bugs, corn flies and corn lice.

Top down – In the top-down framing, climate adaptation relates to decisions that are taken on the basis of simulated global climate scenarios downscaled to a regional level and fed into impact models to estimate potential impacts. Based on this, adaptation measures are then identified and evaluated via multicriteria, cost-effectiveness or costbenefit analysis Transcription factor – In molecular biology and genetics, a transcription factor (sometimes called a sequencespecific DNA-binding factor) is a protein that binds to specific DNA sequences, thereby controlling the rate of transcription of genetic information from DNA to messenger RNA.

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scientific Transgenic event – refers to the unique DNA recombination event that took place in one plant cell, which was then used to generate entire transgenic plants.

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Uncertainty range – Defines an interval within which a numerical result is expected to lie within a specified level of confidence.

Viroids – Viroids are the smallest

infectious pathogens known, consisting solely of short strands of circular, single-stranded RNA without protein coats.

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Impact of climate change on crop production and agricultural engineering technological countermeasures used in mitigation in China Dr Zhao Aiqin, Professor Zhu Ming, Professor Wei Xiuju

Postdoctoral Scientific Research Stations of Chinese Academy of Agricultural Engineering, Chinese Academy of Agricultural Engineering, Chinese Society of Agricultural Engineering, Beijing 100125, China

Summary1 A statistical analysis of the independent effects of a number of environmental factors on agricultural crop production in China indicated that climate change, measured as trends of temperature rise, sunshine decline and precipitation fluctuation, had a depressing effect overall. The increase in temperature is most obvious in the Northeast and North of China with increased precipitation in southern China while the decline in sunshine duration is most significant in the Northwest. The frequency and intensity of extreme weather are increasing; On the one hand, the increased temperature results in the northward and westward expansion of the cropping boundary, thereby increasing the area of arable land by 4.91% of the total in the Northeast from 1981 to 2010; the fertilization effect of CO2 will also increase wheat, rice, and maize yield by about 37%, 15% and 10%, respectively. On the other hand, the temperature rise and precipitation decline aggravate drought and water shortage in most parts of the north, thereby lowering the yield of maize, wheat and soybean by 1.73%, 1.27%, 0.41%, respectively from 1980 to 2002. Crop yield loss caused by agricultural meteorological disasters can be as high as 15% (1980-1993); The expectation is that crop yield loss in China owing to climate change is estimated to be 5%10% in the next 30 years if no control measures are taken. Agricultural engineering, which has shown great benefits to China’s agriculture, is coping, and will cope effectively with adverse effects of climate change on crop production.

Agricultural machinery and land consolidation can stabilize, or increase, planting acreage and water-saving; and irrigation can enhance drought resistance of food production. Agricultural machinery contributed to 18.33% crop yield profit in 1996. An agricultural advisory service provides early warning and forecasting of agricultural disasters. Artificial drying of harvested products can reduce product losses during transporting and processing. Conservation tillage and sub-soiling, during which straw is returned to soils, can also increase food production. In fact, agricultural engineering techniques used in mitigation have made a great contribution to crop yield, compensating for the adverse impacts of climate change.

Key words climate change, China, agricultural engineering technology, mitigation measures, crop production

Introduction

S

ince the 1950s there has been a global warming trend which is escalating (1,2,3,4). China’s warming trend is particularly evident over the last 50 years, during which the surface air temperature has increased 1.1oC, i.e. 0.22oC per decade – values significantly higher than those of global levels (5).

China has a large population and

relatively little arable land, accounting respectively for 20% and 9% of the world total. Food security is thus a major challenge. Both natural and social factors can affect agricultural production. Social factors include agricultural management, agricultural technology, economy, labour and agricultural policies. Their impact on crop yield cannot be ignored. However, natural factors including climate, environment, water resources and

arable land are highly associated with agricultural production. Climate is the main uncontrollable factor affecting crop production (6). This paper considers the effects of the climatic trends on grain production in China by summarizing published data, analyzing regional changes and their relationship with grain production. It also considers the effect of mitigating factors in current use (mainly agricultural engineering technologies) in China.

1 Foundation: Special Scientific Research Fund of Agricultural Public Welfare Profession of China(201203002); CAST (China Association of Science and Technology)Promotion Project for Elite Journals Author: Zhao Aiqin, postdoctor in Chinese Academy of Agricultural Engineering (CAAE). Email:zhaoaiqin@tcsae.org Zhu Ming, ProfessorPresident of Chinese Society of Agricultural Engineering, President of CAAE. Email: mingzhu@agri.gov.cn Wei Xiuju:Professor of CAAE. Email:weixj06@163.com

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Fig 1 Classification of the six main regions and its provinces in China

1 Grain producing regions and regional climate change in China 1.1 Classification of grain producing area China, located in eastern Asia and the western Pacific (73°33'-135°05'E, 3°51'53°33'N), covers an area of about 9.6 million km2 with a complicated range of landforms and climates ranging from tropical monsoon to temperate continental.

For the purposes discussed here grain production is divided into six regions: the Northeast, the Northwest, North China, the middle and lower-reaches of Yangtze River, South China, and the Southwest (Fig. 1).

The Northwest is dominated by a continental air mass, giving it cold winters, hot summers, drought and little rain. The Northeast and North China are affected by a temperate monsoon climate and characterized by cold and dry winters, and hot and rainy summers.

The middle and lower-reaches of Yangtze River and other subtropical regions are affected by a subtropical monsoon climate and often have dry winters with a low temperature, and rainy hot summers. South China and the Southwest are dominated by a tropical monsoon climate, with hot weather throughout the year and with most rain in the summer.

1.2 Regional climate change Climate change in China's grain producing areas has been associated with rising temperatures, declining sunshine, and fluctuating precipitation

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(Table 1) (7). Over the 46 years, the average air temperature has been higher in the northern part of China than the southern part of China and highest in the Northeast and the Northwest with an increase of mean temperature more than twice that of the Southwest.

Sunshine duration decreased in the southern part of China about two to three times faster than in the north.

The decline in sunshine was most obvious in the Southwest.

Precipitation has decreased in northern China and increased in southern.

The temperature rise has been highest in the Northeast and precipitation fluctuates greatly throughout the country.

The precipitation in North China is lower in summer and autumn (8).

These changes aggravate the hot and rainy weather with little sunshine in the southern part of China, and also exacerbate drought conditions of northern China, despite relieving the coldness of northern China, thereby

leading to a negative influence on agricultural production.

As global warming continues it is likely that, weather extremes (minimally, the event lies in the upper or lower ten percentile of the distribution or has destructive potential) such as unusually high or low temperature, prolonged dry or wet conditions, and heavy precipitations occur more frequently and intensively.

During the years of 1956-2009, the highest and lowest daily air temperature each month in mainland China increased at a rate of 0.1 and 0.6oC/10a, respectively, while hot days with a daily maximum temperature > 25oC has significantly increased at a rate of 2.1 days/10a (9).

Over the past 20 years, drought conditions in the Northeast and North China have been more severe, droughts in the eastern part of the Northwest, most of North China, and the eastern and southern part of China have increased, and floods in the middle and lower-reaches of the Yangtze River are becoming more serious (5).

Over the last 50 years, the occurrence of heavy precipitation events in the Northwest have been frequent, the torrential rain days in the middle and lower-reaches of the Yangtze River have increased, and thus it is necessary to pay close attention to the threat posed by floods in summer (5,10).

In a word, in recent years, China’s climate is characterized by temperature rise, a reduction in the duration of sunshine, and increased frequency of extreme weather events, and there are obvious regional differences in these changes over the vast territory of China.

However, average temperature rises in the lower temperature regions can increase the cropping area; but rising temperatures also increase the risks of severe agricultural pest attacks, intensified drought, and water shortages on arable land.

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scientific 2 Effects of climate change and agricultural meteorological disasters on main grain production Climate change has the potential to affect all aspects of crop production in China.

2.1 Positive impact of climate change on agricultural production Higher mean temperature is likely to increase the planting area and thus increases the area of the arable land in northern and the western part of China, by increasing the length of the growing season.

In the past 30 years, the northern cropping boundary has moved northward and westward for winter wheat in the Northeast, North China and the Northwest, and northward for double cropping rice (twice/year); but the boundary extends mostly towards the southeast for winter wheat and summer maize in the middle and lowerreaches of the Yangtze River (11).

Overall, for the northern part of China, the extension of the planting boundary is most obvious in the northeast region (12), and these changes may increase crop acreage, or enhance grain production capacity in each region.

Zhao Jin et al. (13) showed that, compared with 1961-1980, the planting northern boundary of spring maize in the Northeast in 1981-2010 extends northward by 158.3-285.8 km, leading to an increase of 3.87 x 104 km2 in the planting acreage and accounting for about 4.91% of the total arable land area in the Northeast.

Liu Zhong et al. (14) showed that the increase in rice, maize and spring wheat yield of China has resulted from expansion of the planting area, while that of winter wheat is dominated by an increased planting area and yield per unit area, which may result from arable land expansion of the northern boundary.

Agricultural production emits greenhouse gases which contribute to climate warming and the CO2, as a carbon fertilizer, increases grain production. Greenhouse gas emissions (CH4, N2O and CO2 etc.) from industrial development, human population, commercial fertilizers and livestock production continue to increase, and exacerbate global warming. Tan Qiucheng (15) calculated greenhouse gas emissions from agricultural production in China to be ca 1.6 Tkg of CO2 equivalent in 2009, to an increase of 52% compared with 1980.

Fig 2 Crop yield change during 28 yrs due to trends in climate (temperature, solar radiation & precipitation) during 1980-2008, based on median estimate (From Tao et al., 2012)

Total emissions increased by approximately 1.5% per annum, of which 25% were CH4, 52% N2O, and 23% were as CO2. GHG emissions from rice cultivation and livestock production were 0.14 and 0.43 Tkg of CO2e, accounting for 9% and 27% of the total, respectively (15).

Xiong Wei et al. (16) simulated the effect of natural factors such as CO2 on crop yield using CERES models and found that the increase of CO2 can significantly improve crop yield. The CO2 fertilization effect will be greatest in winter wheat with an increase of yield up to about 37%, followed by rice and maize with the maximum yield increases of 15% and 10%.

2.2 Negative impact of climate change on grain production Changes in temperature, precipitation and sunshine affect crop yields (maize, rice, soybean, & wheat). Between 1980 and 2002, the yield/ha of maize, wheat and soybean due to climate trends decreased by 1.73%, 1.27%, and 0.41%, respectively, but increased in rice by 0.56% (17).

Regional variations in wheat yield have been larger, followed by maize and rice. Yields have fallen for maize in the Northeast, the northern part of the North China Plain and the Loess Plateau (18). From 1980 to 2002, maize yield declined in the Northeast by up to 20%, and was also considerable in the northern and eastern parts of China, while the increase in rice yield was significant in the southern and northeastern part of China (Fig 2) (17).

Temperature is an important factor affecting maize yield and an increase during the growing season can lead to yield decline (19). For rice, precipitation increase, temperature rise, and

sunlight reduction can shorten the growth period and lead to a yield decline (20).

Li et al. (21) applied the methods of first-differences and removal of a linear time trend to analyze climate changeâ&#x20AC;&#x2122;s impact on wheat from 1978 to 1995 and found that temperature rise had decreased yield in Hubei and Jiangsu provinces by 37%-41%, and that increased precipitation led to negative impacts of 23%-60% on yield in several provinces, including Guangdong in the southeastern part of China due to reduced sunlight and crop drowning; but yield variations are not correlated with precipitation and temperature fluctuations in most other Chinese provinces.

Overall, the impact of global warming on China's grain production is negative, and the grain yield decline of China as a whole could be up to 5%10% in the next 30 years (22).

Meanwhile, increasing temperature increases some pests and diseases, accelerates the decomposition of soil organic matter, reduces soil fertility and soil moisture.

In the last 40 years, drought conditions from temperature rises and precipitation reduction has posed a threat to wheat growth in North China. For rice, the high temperature can induce seed set, and reduce crop yield.

In 2003 for example, rice in the middle and lower-reaches of the Yangtze River had poor seed set with a yield decline of 30%-70% (23).

2.3 Negative impact of agro-meteorological disasters on grain production Extreme weather can result in varying degrees of damage to crop growth and yield. Studies have shown that compared to 2002, the extreme

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scientific weather in 2003, such as a cold winter and spring, reduced sunshine or high rainfall, can delay the sowing and jointing date, respectively for 4-6 and 12-15 d, reduce the number of stems per unit area by 29.1%, and decreased biomass, leaf area index, spike numbers, leading to a reduction of winter wheat yield by 9.57% (24); a high mean temperature in the filling stage of winter wheat in May (higher than 21oC) cuts the yield of winter wheat by more than 13%, while severe drought results in a reduced winter wheat yield of <4 t/ha (25). In general, maize has a weaker capacity to cope with climatic fluctuation than either rice or wheat (26).

Agro-meteorological disasters, extreme weather events, pests and diseases occur frequently and now expand to a wider area with the more severe damage, increasing the instability of agricultural production and seriously threatening the safety of China's grain production. Shi Peijun et al. (27) showed that crop yield damage caused by drought, wind, hail, frost and pests during the period 1980-1993 accounted for about 15% reduction in the total yield, (40%) nearly half of which was caused by extreme climatic fluctuation. In the past 50 years, both flood and drought in China have caused annual grain yield loss of 2.06 million tonnes, i.e. an average grain loss of 4.7% a year (28), showing a trend of deterioration (Fig. 3) (29). Snow at the beginning of 2008 in the southern China led to crop damage over 11.8 million ha in 21 provinces, and to crop failure over 168.67 ha. From a regional perspective, grain yield losses caused by weather had been greater in northern than in southern China, more severe in the north, the northeast, and in the northwest, where the grain loss trend had been most severe (29). It is estimated that the yield in China of wheat, rice and maize will decrease in

the next 30 years (22).

2.4 Uncertainty of climate change impact on agricultural production Agricultural production is a complex dynamic system dependent on soil fertility, crop species, climate, technology, management and other factors. Studies on the relationship between climate change and agricultural production contain many uncertainties.

1) Effects of temperature and precipitation on yield are partly dependent on soil conditions. In a 3 year experiment, the yield-increasing effect of nitrogen fertilizer for maize is significant when precipitation is higher than 280 mm during the growing season, but is not significant when precipitation is less than 280 mm (30).

In addition, soil drought also affects the yield.

2) Analysis suggests that 5.15% variation in grain yield in China is due

Fig 3 Proportion of grain yield adversely affected by climatic disasters in China from 1950 to 2002, indicating an increasing rate/period (From Li Maosong et al., 2005)

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to grain production policies (such as policies of land use, tax, insurance, and allowance) between1978-2012, for which wheat yield is most sensitive (26); Chinese rice yields are affected by technological change (such as fertilizer, tillage, variety breeding, cultivation, irrigation), social (policy, market, et al.) and natural (climatic fluctuation) factors, accounting for 28%-35%, 15%-17%, and 48%-56%, respectively, of rice yield variation (31).

3 Agricultural engineering technological countermeasures to cope with climate change Agronomic and agricultural engineering technology can help to ameliorate the effects of climate change (see Table 2).

3.1 Agricultural engineering Each of the techniques listed in Table 2 plays important role in agricultural production. The elasticity coefficient (indicating contributions of input factors to yield) of machinery investment, and land and irrigation technique to grain yield is 0.4 and 0.18, respectively (32).

These show the positive effects of agricultural machinery on production by replacing hand labour and increasing the planted area. Hong Renbiao et al. (2000) estimated that agricultural machinery contributed 18.33% to crop yield profit in 1996 based on yield profit, (33).

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scientific Yang Qing et al. (2000) showed that the contribution of agricultural machinery to crop yield profit increased from 16.6% in 1990 to 21.1% in 1998 in Shaanxi province (34). In addition, utilization of drainage and irrigation machinery reduced by 10% in drought-suffering areas and by up to 20% in flood-suffering areas in 1996 (35).

Water-saving and irrigation are also helpful in increasing production efficiency, and more importantly, by reducing the effects of drought on grain yield. A survey reported that 70% of grain, and 90% of vegetables and fruits are produced from irrigated fields in China (36). A 2008-2009 academic report of Chinese agricultural engineering development revealed that agricultural water allocation schemes could add 129 billion tonnes of irrigation water through comprehensive application of watersaving, irrigation, and rain-harvesting techniques (37). Increased irrigation water resources has led to crop yield increases, especially in arid areas. Hou Peng et al. (38) estimated the potential of irrigation to increase maize yield in Heilongjiang. The potential was highest in the southwestern part of the region and decreased to the east and north. Those regions with high cumulative temperatures (> 2500oC â&#x20AC;˘ d) and low precipitation (350 mm during the growing season), amounting to 430 mm/an., are ready for the establishment of irrigation.

Early-warning systems, help reduce the risk of pests and of agricultural meteorological disasters, and have been successfully practiced (39).

Obviously, the resource of arable land is lessened by social and economic development. Comprehensive land use planning can increase the area of arable land. By the end of 2005, about 1.58 million ha of land in China have been subjected to consolidation, resulting in an additional 370,000 ha farmland (40).

It is estimated that inappropriate, or the absence of processing post-harvest causes a loss of about 17% of vegetables and can cause up to 10% loss of harvested grains, about 80% of which is lost at the harvest site, equivalent to the produce of 1.8 Mha of land (41). Agricultural products processing techniques can dry, clean, grade, pack, store, and transport agricultural products by mechanical, or physical, methods with high efficiency, and they can reduce product loss during the harvesting and transportation and can improve their

utilization efficiency.

3.2 Agronomic practice and agricultural engineering Appropriate agronomic practices to increase crop yield vary in different regions of China.

1) Conservation tillage

A 10-year experiment in the Loess Plateau of Shanxi province and in dry farming areas of Jinzhong and Jinnan basin showed that conservation tillage can reduce soil erosion by around 80%, increase soil organic matter by 0.03%-0.06%, and increase grain yields by 13%-16% (40). In the central Songliao Plain, no-till and wide and narrow row spacing can significantly increase maize yield compared with rotary tillage and ploughed fallow, and thus the former method is recommended for arid areas (42).

In the loess Plateau, a series of tillage techniques for rainfall harvesting, ridge-furrow-mulching and supplementary irrigation are effective measures to cope with an arid environment and improve crop yield, which is summarized by Rui-Ying Guo and Feng-Min Li in World Agriculture (43).

2) Deep ploughing with returning straw into soil

Studies in North China showed that compared to conventional tillage only, subsoiling can increase winter wheat by 8.04%. However, when straw is ploughed into the soil, subsoiling can increase soil moisture content and wheat yield by 16.07 % and 11.45%, respectively (44).

3.3 Other agricultural techniques â&#x20AC;&#x201C; chemical fertilizer, pesticide, and mulching film The beneficial effects of commercial fertilizers, pesticides and plastic film

have become less positive in recent years in China. Increasing incidence of pesticide tolerance has increased pest damage and normal plastic mulching film left in the soil detracts from the physical property and organic matter of the soil, causing pollution and reducing soil fertility, which reduces grain yield.

Less use of normal plastic film, replaced by degradable plastic film, or mechanization of residual plastic recycling technology are countermeasures for agricultural plastic film residue pollution (45).

Technical approaches to improve fertilizer management at farm level have been summarized by David Powlson et al. (2014) (46). It is necessary to control the amount of fertilizers applied, control pesticides and plastic mulching film to maximize crop yield, whilst avoiding damage to the environment.

It is necessary to develop a sustainable fertilizer programme to relieve and offset the negative influence of climate change on grain production while taking full advantage of the CO2 fertilization effect.

4 Conclusions 1) There is clear evidence of a general rise in surface air temperature in China, especially in the north region where the mean annual temperature increase has been 1.6 times that in the southern region over the last 40 years.

However, changes in precipitation rate vary, with a decline in the North East, the North West, and North China but with an increase in the middleand lower-reaches of the Yangtze River.

Changes in temperature and precipitation rate are most significant in the Northeast.

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scientific The frequency and intensity of extreme weather are both increasing.

2) Climate change has positive and negative impacts on China’s grain production. The increased temperature allows the northward and westward expansion of the cropping boundary, thereby increasing the cold area of arable land. An increase in arable land area accounts for 4.91% of the total in the Northeast between 1981 and 2010. The fertilization effect of CO2 is estimated to have increased grain yield by 37%, 15% and 10% for wheat, rice, and maize, respectively. The negative effects of climate change on grain production include temperature rise and precipitation decline which aggravate drought and water shortage in most part of the north, affecting crop yield. Overall, the negative impact of climate change is dominant in China, with yields of maize, wheat and soybean reduced by 1.73%, 1.27%, 0.41%, respectively, from between 1980 to 2002. Grain yield loss is most severe in the Northeast, and the potential for maize production is decreasing in the Northwest and North China. Wheat yield has declined in the Yangtze River and in South China.

Crop yield loss caused by meteorological disasters can be as high as 15% (1980-1993). The overall crop yield loss in China, over the next 30 years, owing to climate warming is estimated to be 5%-10% if no measures are taken.

3) Agricultural engineering technology, or in combination with agronomic practices, is an effective measure to increase both the area cultivated and crop yields. Agricultural machinery plays an important supporting role in ensuring the steady increase in planting acreage. It contributed 18.33% to crop yield profit in 1996. Water-saving and irrigation can reduce the effects of drought and information systems can provide early warning and forecasting of impending problems. Land consolidation can increase the available land area and effective arable land.

New post-harvesting practices of agricultural products reduce product loss during harvesting, transporting and processing. Conservation tillage in arid and semi-arid areas has significant effects of water retention, soil conservation, and on the effectiveness of fertilizers. It reduces soil erosion by around 80% and has increased grain yield by 13%-16% in Shanxi province.

In the Northeast and North China, subsoiling plus ploughing straw into soils has been shown to also increase

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wheat yield by 11.4%. Therefore, in the future, development and application of advanced agricultural engineering technology will be an important method of maintaining and increasing crop yield in China.

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The challenge of breeding for increased grain production in an era of global climate change and genomics Professor Wallace A. Cowling, The UWA Institute of Agriculture, The University of Western Australia, 35 Stirling Highway Crawley, WA 6009 Australia Summary Grain from self-pollinating crops contributes more than 60% of annual global calories for human consumption, and demand is increasing. Grain production in self-pollinating crops is extremely sensitive to the effects of heat and drought, and these stresses are increasing as a result of global climate change. The rate of increase in grain yield of the world’s major self-pollinating crops is not keeping pace with demand and is threatened by global climate change. Heat and drought stress tolerance are complex traits often present in wild or landrace relatives of crop plants. They are difficult to transfer by traditional breeding methods. Genomic selection may help this process, but depends on high genetic diversity with associated molecular marker polymorphism in elite breeding populations. The confluence of human population increase, climate change and genomics necessarily promotes change in the underlying methods of crop breeding. The ‘animal model’ provides best linear unbiased prediction (BLUP) of breeding value based on information from all relatives in the pedigree across cycles of selection. A plant version of the animal model is advocated for self-pollinating crops to increase genetic diversity in breeding populations and accelerate response to selection. BLUP or genomic BLUP values can be estimated for drought and heat-stress tolerance, grain yield and quality and predictions are improved by integrating data across cycles of selection. Genetic diversity is retained, the rate of inbreeding can be controlled and the potential for longterm genetic gain is increased. Pure lines will “spin off” from this rapid early generation crossing programme. This change in crop breeding methods is motivated by the need to improve response to selection, including genomic selection, for grain yield in the face of global climate change.

Key words crop breeding, genetic diversity, animal model, pedigree selection, genomic selection, optimum contribution selection, heat stress, drought stress

Glossary Additive genetic variance: the heritable genetic variance in a population. If selection occurs during the selfing process, additive genetic variance is lost from the population and cannot be restored when selection ceases. Traditional breeding methods for self-pollinating crops cause a permanent loss of additive genetic variance when crossing occurs after selfing and selection of pure lines (see reference 12).

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Animal model: a process of recording the phenotype and pedigree relationships in animal breeding (also used in perennial crop species), which provides best linear unbiased prediction (BLUP) of the breeding value of an individual based on all the phenotypic information available from itself and its relatives in the pedigree down to the base population. The animal model has revolutionised commercial animal breeding over the past 30 years, with

increased rate of genetic improvement while controlling the rate of population inbreeding through additional selection schemes such as optimal contribution selection.

BLUP: best linear unbiased prediction. In the animal model, the BLUP value is a selection index based on all the phenotypic information available from itself and its relatives, and is the predicted breeding value of the individual. Various software programs have been developed to

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scientific calculate BLUP values in animal breeding programmes, and this software can be used in selfing crops.

F1 individuals: first filial generation (F1) individuals are the direct descendants of a cross. In animals, the parents are heterozygous and each F1 individual in a family is genetically unique (unless the sibs are identical twins). In traditional selfing crop breeding programmes, the parents are pure lines and F1 individuals from a cross are genetically identical. In this article, the animal model is applied to selfing plants, and the parents used for crossing are heterozygous and not pure lines, and their progeny are referred to as F1 individuals.

Alternatively, “S0” can be used for the direct descendants of a cross between two heterozygous parents of a selfing crop species.

Genomic selection: a modification of the animal model, where, instead of pedigree information, a “realized” relationship matrix is constructed from whole-genome molecular marker data on the individual and its relatives in the pedigree tree. The genomic predicted breeding value is often referred to as G-BLUP. Potentially, this increases the accuracy of predicted breeding values.

Heterozygous: individuals with different forms of a gene from the mother and the father. F1 individuals are described as heterozygous for this gene.

Homozygous: individuals with only one form of a gene, either from the mother or the father. Pure lines are highly homozygous because they are the product of many generations of selfing.

Inbreeding: in selfing crops, inbreeding causes a loss of genetic diversity known as genetic drift, or the random loss of alleles due to small population size. Selfing crops do not suffer from “inbreeding depression” as observed in animals and crosspollinating plants. This loss of genetic diversity limits long-term genetic gain in selfing crops. For more evidence on low effective population size and genetic drift in selfing crops, see references 8 and 9.

Optimum contribution selection: a selection method developed in the animal model which optimises genetic diversity and potential for long-term genetic gain in the breeding population. Individuals are selected for mating based on their BLUP or genomic BLUP value plus their potential to minimise inbreeding and contribute to long-term genetic gain.

See reference 16.

Phenotype: the value of a trait measured on an individual.

Phenotyping for heat or drought tolerance could occur on an F1 individual, or its self progeny, in the plant version of the animal model.

Grain quality or grain yield must be measured on the self progeny of an F1 individual, in order to have enough seed to evaluate grain yield or quality in field plots.

Polymorphism: used in relation to molecular markers when they detect heterozygous molecular marker ‘genes’. Low rates of polymorphism reflect high rates of inbreeding.

Pure lines: these are formed when F1 individuals are selfed for several generations to produce nearhomozygous lines that breed true.

Selfing crops: crops which produce grain by self-pollination. This includes most of the world’s most important grain-producing crops, such as wheat, barley, oats, soybean, millet, sorghum and canola. Selfing crops have traditionally been crossed after selection of pure lines, a process which may last several years and thereby slow genetic progress.

Self-pollination: this occurs when the male gamete (pollen) fertilizes the female gamete (ovule) on the same plant and produces seed from selfpollination. In cross-pollinating species such as lucerne (alfalfa), rye or turnip rape, seed production is the result of cross-pollination and self-pollination normally fails.

Introduction

M

ore than 60% of calories for human consumption are produced annually by selfpollinating grain crops (1).

The 2009 Declaration of the World Summit on Food Security stated that a 70% increase in agricultural output was required by 2050 to keep pace with population increase, and that agriculture needed to adapt to climate change through a sustainable use of genetic resources (2). Two serious threats to food production are the effects of heat and drought stress on selfing crops. New sources of heat and drought tolerance are often found in wild or landrace accessions stored in genetic resource centres (3, 4), but such traits are complex and specific strategies of crop breeding are necessary to exploit these valuable alleles in elite crop breeding programmes (5). New breeding technologies, such as genomic selection, may help to increase food production (6, 15), but this is limited

by the underlying breeding methods for selfing crops which are not sustainable in the long term (8). A new crop breeding method, based on the animal model, was proposed recently for long-term and sustainable genetic improvement in selfing crops.

The method used all genetic relationships (with crossing and selfing in the pedigree) across cycles of selection, plus additive and nonadditive genetic covariances between cycles, and accelerated response to selection for a low heritability trait (9).

Genomic selection could add value to the process.

The confluence of several factors – increasing human population, increasing demand for calories from selfing crops, climate change leading to heat and drought stress, potential reductions in grain yield due to heat and drought stress, and new genomic technologies – necessarily stimulates change in the underlying breeding method for selfing crops.

Background The traditional breeding method in selfing crops has been described as “selfing before crossing” (9).

This method developed naturally in selfing annual crops, where new cultivars are identified after selfing and selection of inbred lines, and are then used in crossing. New cultivars of selfing crops “breed true” – farmers confidently retain seed knowing that seed harvested from a pure line is genetically identical to seed that was sown. Selfing and selection of superior homozygous pure lines is the ultimate goal of breeding in self-pollinating crops (10, 11).

However, this method of breeding selfing crops limits long-term and sustainable genetic gain (8). “Selfing before crossing” results in huge numbers of potential genotypes for testing and selection after each generation. Selection reduces the numbers of genotypes for testing during selfing, often with the aid of breeding technology (6).

Consequently, new pure line cultivars represent just a small fraction of the genetic variation available in each cross, and usually only a few elite lines are used as parents in the next round of crossing. These few elite parents with few ancestors in their pedigree, restrict the effective population size in selfing crops (8, 9).

In outcrossing species, such as animals or perennial trees, selection occurs on F1 individuals – where each sib from a cross is genetically unique

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scientific and has a unique breeding value that can be predicted by phenotyping.

Only superior individuals are used in crossing, and these are identified on the basis of information from all relatives in the pedigree through a process that has become known as the ‘animal model’ (7). In such species, additive genetic variance in the population can be restored when selection is removed (12). This is not the case with selfing crops, because additive genetic variance in the breeding population is reduced by selection during selfing, and this reduction in additive genetic variance is inherited by future generations (12, 13). As a result, selfing crop breeding programmes tend to have lower genetic diversity than animal breeding programmes and longer generation intervals (8). This limits both shortterm and longer-term responses to selection.

Elite crop breeding programmes also tend to become remote, genetically speaking, from wild and landrace accessions which may store many valuable genes (such as genes for heat and drought tolerance). This increases the difficulty of migrating minor alleles for complex traits with low heritability from wild into elite breeding populations – and special breeding methods are needed to “bridge the gap” between wild or landrace accessions and elite crop breeding programmes (5, 14).

The reaction of most researchers and funders has been to seek technological solutions to these problems (6, 15).

However, the underlying methods of breeding selfing crops should be critically examined first. When the ‘animal model’ (7) was used in a selfing crop, this dramatically improved the rate of response of selection to a complex trait over two cycles of selection (9). The method used pedigree and phenotypic information from relatives back to the base population. Such a method could be used to improve yield under heat and drought stress in elite crop breeding programmes. The method will provide a sound basis for genomic selection in selfing crops, for the introduction of new alleles from wild and landrace relatives, and for the conservation of additive genetic variance for long-term genetic gain. As with all breeding methods, appropriate selection methods are critical to minimise inbreeding. Optimal contribution selection was developed for long-term genetic gain in the animal model (16),

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Generation 1

Generation 2

Generation 3 Fig 1. A typical pedigree in the animal model. Circles represent females and squares represent males. Every individual is an F1. In the animal model, data are analysed across cycles of selection using information from relatives in the pedigree. In each generation, F1’s with required traits are selected for crossing.

but the software would need to be modified to include selfing in the pedigree. The animal model has a lot to offer breeding of selfing crops.

Using the animal model in selfing crop plants The animal model uses information from relatives including ancestors, collateral relatives, and progeny to find the best linear unbiased prediction (BLUP) of breeding value for an individual (7).

BLUP is a selection index based on information from all relatives in the pedigree back to the base population, and has been very successful in breeding of animals and perennial tree crops where it has increased the rate of genetic gain without a large increase in inbreeding (8, 9). In a typical animal pedigree (Fig. 1), the F1 offspring inherit half their genes from the dam and half from the sire, and because every parent is heterozygous, then every F1 sibling is heterozygous and genetically unique. The relatives that contribute most to accuracy of BLUP values are parents, full-sibs and progeny, and the individual itself, but accuracy can be improved by including collateral relatives (full sibs, half-sibs), repeated records, and records from correlated traits (17).

A typical animal breeding programme has a database with names of individuals and records of phenotypic measurements on each individual at each date of measurement. In addition, there is a record of the names of each parent for each individual (the pedigree records).

These data grow over time, when new individuals are added at birth. Most plant breeders have records which are similar to animal breeders, with both phenotypic and pedigree records on individuals. Many public or commercial databases are available to help plant breeders manage such records.

The entire data set in animal breeding, including many cycles back to the base population, are analysed by software programs such as ASRemlR (25) to solve linear mixed models including phenotypic and pedigree records across cycles of selection to arrive at the BLUP of breeding value for each individual. The BLUP value is based on all records over all generations in the ancestry. This type of analysis over cycles of selection has not been attempted before in plant breeding until recently (9), where it was based on similar linear mixed models solved using ASReml-R software.

In traditional annual crop breeding programmes, cross progeny are selfed for several years after crossing (Fig. 2).

This delays selection of suitable lines until superior inbred characteristics are identified, and slows selection cycles.

As indicated above, crossing after selection reduces additive genetic variance available for future genetic gain. These differences between typical animal and plant breeding programmes are represented by the sheep (F1 individuals) and

Long crossing cycles. The initial cross is followed by up to 12 years of selection of required traits

Fig 2. Crossing in traditional breeding programmes of self-pollinating annual crops is followed by several years of selfing. This leads to long cycle times (typically 6-12 years), low effective population size and a permanent loss of additive genetic variance in the population as a result of selection during selfing.

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Fig 3. In animal breeding programmes, represented here by sheep in Australia, the phenotyping and selection is done on F1 individuals. In traditional selfing crop breeding programmes, represented by plots of canola lines in the rear of the picture, the phenotyping and selection is done during several generations of selfing, followed by yield and quality testing on F6-derived or later material.

canola (replicated F6 plots) in Fig. 3.

In a recent example in a selfing crop (9), only one generation of selfing occurred between each cross (9) (Figure 4). The method was tested in field peas, Pisum sativum, the same species used by Mendel to demonstrate the nature of inheritance (9). In annual selfing crop plants, the plant undergoes sexual reproduction during the growing season, normally before phenotypic records are obtained. Therefore it is not possible to phenotype, select and use F1 individuals for crossing before flowering. In the method developed

by Cowling et al. (9), F1 plants were assessed for resistance to a lowheritability trait, ascochyta blight disease (Didymella pinodes complex), during the growing season. Self set seed was harvested from selected F1 plants and grown on to be used for crossing. Data were analysed over two cycles of selection, and analysis was improved by including genetic relationships and the additive and nonadditive genetic covariances between cycles. High accuracy of predicted breeding values (r > 0.8) was achieved on F1 plants, with >11% increase in disease resistance forecast in the next

selfing plants

Fig 4. The cycle time in a traditional breeding programme of annual self- pollinating crops is shown on left taking at least 6 years, compared with the plant version of the animal model where the cycle time is just 2 years. The short cycle time on the right accelerates response to selection, and accuracy of prediction of breeding values is improved when the data are analysed across cycles according to the animal model (see reference 9).

cycle based on 20% selection proportion (9).

Cowling et al. (9) incorporated both selfing and crossing in the pedigree of their ‘plant model’ version of the animal model (Fig. 4). The plant model conformed to assumptions of the animal model with information combined across cycles of selection back to the base population, thereby avoiding the problem of bias in selection (18). The key advantages of the method were shortened selection cycles compared with traditional methods (Fig. 4) and potentially high genetic diversity in the breeding population, because many heterozygous parents were used in crossing. Another key benefit was the presence of many “augmented halfsibs” – self and cross progeny related by a common parent. Such relationships have never before been used in the animal model, and increased accuracy of prediction of breeding values in F1 individuals (9).

The method could be adapted to traits measured in harvested seed of F1 plants, such as grain yield or quality under heat and drought stress.

However, to avoid high rates of population inbreeding, the selection of parents used in crossing should be optimised to retain genetic diversity and improve prospects for long-term genetic gain. Such a method developed for the animal model is “optimum contribution selection” (16), and this method could be modified to include selfing in the pedigree of the plant model.

Genomic selection may increase accuracy of predictions of breeding value compared with BLUP selection as it has done in the animal model (19, 20). This potential in plant breeding has stimulated many recent reviews, although the potential value is limited by population structure and lack of polymorphisms in breeding populations of selfing crops (21). No current models of genomic selection in selfing crops integrate training of molecular markers on selection candidates across generations (22), although this is now possible in the plant version of the animal model (9).

The potential value of genomic selection should always be evaluated against BLUP selection based on pedigree relationship information (23).

Genomic BLUPs, based on phenotypic data and molecular genetic relationship information across generations, have been used to advantage in the animal model (19, 20).

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scientific The plant version of the animal model does not interfere with the selfing process in crop breeding, which can proceed as normal with the selection of pure lines from each generation (Fig. 5).

Crossing will occur with self progeny of superior F1 individuals, but normal procedures of selfing will occur on these parent plants to develop superior pure lines. Therefore, the plant version of the animal model requires only small modifications to existing breeding programmes. The key change is to cross selected F1 individuals or their self progeny, based on their BLUP breeding values from analysis across cycles as in the animal model (9). For traits such as grain yield or quality, the phenotype must be measured on self progeny of F1 individuals.

Problems to be confronted Research has begun on using the animal model to integrate data across cycles of selection in selfing crops (9), but much work remains to be done, for example, to modify the model for yield and grain quality measured on self seed of F1 individuals.

Research will be necessary to optimise selection based on BLUP values and genetic relationships, in order to minimise population inbreeding. It is not wise to rely on truncation selection as this will quickly narrow the population through inbreeding.

Developments in optimum contribution selection will be necessary to include selfing in the pedigree, which has not been done before (16).

Plant breeders will need to be familiar with software for spatial trial design such as DiGGer (24), to assemble pedigree information accurately on a database (including selfing), and to analyse field trial data and pedigrees across cycles of selection using software such as ASReml-R (25).

A full example of the model, including the phenotypic data, pedigrees with selfing and code for ASReml-R, is provided by Cowling et al.

(9). In this authorâ&#x20AC;&#x2122;s experience, the cost of the method is not significantly different from previous annual budgets in crop breeding, and costs can be substantially reduced if doubled haploidy or genomic technology is not affordable in the programme. This makes the method ideal for developing countries where plant breeding budgets are low, but demands for improvements from selfing crop breeding are high. For existing high-

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cultivars

cultivars

cultivars

Fig 5. The plant version of the animal model allows selfing to continue on parent plants used in crossing (selfs of F1 individuals). The selfing programme continues â&#x20AC;&#x153;as normalâ&#x20AC;? to produce and identify elite pure lines and cultivars, but crossing of the selfs of superior F1 individuals has already occurred several years earlier. This potentially accelerates response to selection and retains more genetic diversity and additive genetic variance for future generations.

cost breeding programmes, the method involves relatively minor modifications to existing procedures, but the change will be highly rewarded with increased genetic diversity and greater long-term genetic gain compared with current breeding methods for selfing crops.

Impact on policy The method outlined here requires little additional funding and can be used by all countries (developed or developing) and structures of funding (public and private).

Some breeding programmes are funded entirely by public sources, and others are entirely funded privately.

Most breeding programmes rely indirectly on publicly funded research, especially in the area of pre-breeding which develops new sources of genetic diversity. All selfing crop breeding programmes are united by their dependence on breeding methods developed over 100 years ago. The new method outlined here is flexible and can be applied equally to prebreeding programs and to elite commercial programmes. The method was developed in animal breeding, and requires only minor adjustments to apply to selfing crop plants. The plant model advocated here differs in the timing of crossing to traditional breeding of selfing crops. Crossing will occur on F1 individuals or their self progeny, selected for required traits, before extended selfing and selection of pure lines. This will improve longterm genetic gain and minimise loss of additive genetic variance through low effective population size.

Plant breeders and funders may ask: our breeding methods have worked for securely for over 100 years, so why

change now? Why not continue selfing then crossing with elite pure lines? It is true that genomic selection or other molecular genetic technologies could help future advances in crop breeding (6, 15).

However, global food production and human survival in an era of rapid climate change demands critical examination of the underlying method of selfing crop breeding, which could exploit methods developed in the animal model to achieve better results in the medium and long-term. Loss of additive genetic variance by selection during selfing before crossing (11) is crippling genetic advance in many breeding programmes, and limits our ability to improve complex traits such as heat and drought tolerance.

Genomic BLUPs may improve selection accuracy compared with pedigree BLUPs in selfing crops, but both genomic and pedigree methods of prediction require optimum contribution selection techniques to improve long-term genetic gain, as in animal breeding (22). Prediction of breeding value, by itself, is only one part of successful long-term genetic gain. For pre-breeders, the method would be used to retain genetic diversity from a wide range of sources, such as landrace or wild types with heat and drought tolerance, while improving the population across cycles of selection with large effective population size. Selection will focus on the trait(s) of interest, such as drought or heat tolerance, and large effective population size will reduce population inbreeding and loss of alleles by genetic drift. Pre-breeders should incorporate elite cultivars into their programmes, to keep the agronomic attributes relevant to elite commercial programmes in the target region.

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scientific For commercial breeders, the method provides an opportunity to achieve better results than competitors in the medium and long-term.

This applies equally to hybrid breeders as it does to breeders of selfing crops. Most hybrid breeders use selfing to produce superior inbred parent lines. The method outlined here allows migration of new genetic diversity and greater retention of existing genetic diversity – both of which set up the programme for longterm genetic gain in an era of changing climates.

A wise policy on the part of administrators and funders of crop breeding, both public and private, would be to compare traditional breeding alongside the new approach outlined in this article. Breeders normally do not require too much incentive to “try something new” if given support from their leaders.

The changes proposed here to existing crop breeding programmes (Fig. 5) are relatively small, but breeders need to see “proof of concept” to adopt new breeding methods and new technologies. Proof of concept is now available for the new breeding method (9), and the next step is to encourage governments and private companies to invest in the training, computers and software to allow breeders to compete and achieve the best results in the medium to longterm. Such changes have occurred in animal breeding over the past 30 years. If implemented now, the plant model will lead to significant improvements in 5-10 years, but these improvements will accumulate into the future to contribute to the 70% increase in agricultural output proposed by the 2009 Declaration of the World Summit on Food Security by 2050, with a sustainable use of genetic resources (2).

Conclusions

References

A new breeding method based on the animal model is proposed for selfing crops, where crossing occurs before extended selfing and selection of inbred lines. The method promises to improve medium to long-term genetic gain, with higher genetic diversity and more rapid response to selection. The method will reduce the loss of additive genetic variance which happens when selfing and selection occurs before crossing of elite pure lines (12).

The procedure can be implemented readily with training in software programs for trial design (24) and analysis (25). Databases can be modified to include pedigree information on individuals and their relatives, as well as phenotypic data.

The main difference to the animal model, which animal breeders have been developing during the past 20-30 years of BLUP selection, is the inclusion of limited selfing in the pedigree tree (see reference 9). Therefore, most of the developments in the animal model are readily transferred to this new method in selfing crops.

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