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Bioenergy from sugarcane production is considered a key mitigation strategy for global warming. Improving its representation in land surface models is important to further understand the interactions between climate and bioenergy production, supporting accurate climate projections and decision-making. This study aimed to calibrate and evaluate the Joint UK Land Environment Simulator (JULES) for climate impact assessments in sugarcane. A dataset composed of soil moisture, water and carbon fluxes and biomass measurements from field experiments across Brazil was used to calibrate and evaluate JULES-crop and JULES-BE parametrisations. The ability to predict the spatiotemporal variability of sugarcane yields and the impact of climate change was also tested at five Brazilian microregions. Parameters related to sugarcane allometry, crop growth and development were derived and tested for JULES-crop and JULES-BE, together with the response to atmospheric carbon dioxide, temperature and drought (CTW-response). Both parametrisations showed comparable performance to other sugarcane dynamic models, with an RMSE of 6.75 and 6.05 t ha-1 for stalk dry matter for JULES-crop and JULES-BE, respectively. The parametrisations were also able to replicate the average yield patterns observed in the five microregions over 30 years when the yield gap factors were taken into account, with the correlation (r) between simulated and observed interannual variability of yields ranging from 0.33 to 0.56. An overall small positive trend was found in future yield projections of sugarcane using the new calibrations, with exception of the Jataí mesoregion which showed a clear negative trend for the SSP5 scenario from the year 2070 to 2100. Our simulations showed that an abrupt negative impact on sugarcane yields is expected if daytime temperatures above 35 oC become more frequent. The newly calibrated version of JULES can be applied regionally and globally to help understand the interactions between climate and bioenergy production.

Irrigation strategies are keys to fostering sustainable and climate-resilient rice production by increasing efficiency, building resilience and reducing Greenhouse Gas (GHG) emissions. These strategies are aligned with the Climate-Smart Agriculture (CSA) principles, which aim to maximize productivity whilst adapting to and mitigating climate change. Achieve such mitigation, adaptation, and productivity goals- to the extent possible- is described as climate smartness. Measuring climate smartness is challenging, with recent progress focusing on the use of agronomic indicators in a limited range of contexts. One way to broaden the ability to measure climate-smartness is to use modeling tools, expanding the scope of climate smartness assessments. Accordingly, and as a proof-of-concept, this study uses modeling tools with CSA indicators (i.e., Greenhouse Intensity and Water Productivity) to quantify the climate-smartness of irrigation management in rice and to assess sensitivity to climate. We focus on a field experiment that assessed four irrigation strategies in tropical conditions, Continuous Flooding (CF), Intermittent Irrigation (II), Intermittent Irrigation until Flowering (IIF), and Continuous soil saturation (CSS). The DNDC model was used to simulate rice yields, GHG emissions and water inputs. We used model outputs to calculate a previously developed Climate-Smartness Index (CSI) based on water productivity and greenhouse gas intensity, which score on a scale between−1 (lack of climate-smartness) to 1 (high climate smartness) the climate-smartness of irrigation strategies. The CSS exhibited the highest simulation-based CSI, and CF showed the lowest. A sensitivity analysis served to explore the impacts of climate on CSI. While higher temperatures reduced CSI, rainfall mostly showed no signal. The climate smartness decreasing in warmer temperatures was associated with increased GHG emissions and, to some extent, a reduction in Water Productivity (WP). Overall, CSI varied with the climate-management interaction, demonstrating that climate variability can influence the performance of CSA practices. We conclude that combining models with climate-smart indicators can broaden the CSA-based evidence and provide reproducible research findings. The methodological approach used in this study can be useful to fill gaps in observational evidence of climate-smartness and project the impact of future climates in regions where calibrated crop models perform well.

Climate change is causing problems for agriculture, but the effect of combined abiotic stresses on crop nutritional quality is not clear. Here we studied the effect of 10 combinations of climatic conditions (temperature, CO2, O3 and drought) under controlled growth chamber conditions on the grain yield, protein, and mineral content of 3 wheat varieties. Results show that wheat plants under O3 exposure alone concentrated + 15 to + 31% more grain N, Fe, Mg, Mn P and Zn, reduced K by − 5%, and C did not change. Ozone in the presence of elevated CO2 and higher temperature enhanced the content of Fe, Mn, P and Zn by 2–18%. Water-limited chronic O3 exposure resulted in + 9 to + 46% higher concentrations of all the minerals, except K. The effect of climate abiotic factors could increase the ability of wheat to meet adult daily dietary requirements by + 6% to + 12% for protein, Zn and Fe, but decrease those of Mg, Mn and P by − 3% to − 6%, and K by − 62%. The role of wheat in future nutrition security is discussed.

Crop modeling is affected by parameter uncertainty. We proposed a framework that integrates sensitivity, uncertainty and parameter calibration of crop models, to provide prediction intervals in place of single values for decision-makers to reduce management risks in agriculture. The framework includes four steps: (1) set prior distributions of parameters and collect measured data, (2) use Morris screening to find out sensitive parameters, (3) adopt Metropolis-Hastings within Gibbs algorithm to calculate posterior distributions of the sensitive parameters and model residual errors, and (4) analyze uncertainties propagation and their applications. The framework was firstly applied on 27 parameters of AquaCrop (version 6.1) on maize in four irrigation scenarios in arid Northwest China, given 5 time series and summary variables including canopy cover (CC), aboveground biomass (Bt), soil water content (SWC), daily evapotranspiration (ET) and final yield (Y) with 1458 measured data points of 27 irrigation treatment-year combinations from 2012 to 2015. The results showed that water stress parameters in AquaCrop were more sensitive in severe drought situations than in full irrigation conditions. The parameter uncertainty brought more variation to simulated final yield than simulated time series variables of maize in arid Northwest China. Model residual error was found to be the major contributor to overall prediction uncertainty, and interannual variation and severe water stress increased its contribution. Adding high-quality measured data of time series variables into MCMC iterations can make the estimated parameters more reliable and more biologically significant. Medians of outputs using the framework were generally closer to the corresponding measurements when compared with the results of using trial and error method. Especially for SWC and Y, Nash–Sutcliffe coefficient (EF) improved from 0.364 to 0.739 and from 0.055 to 0.415, respectively. The framework is straightforward to be applied to other crop models that can be run in batches.

Over the last three decades, few studies have been conducted to tackle the complexity and heterogeneity of Bangladesh farming systems. We address these research gaps with a new survey. Accordingly, a survey was conducted in North-Western Bangladesh to understand how socio-economic traits influence technology adoption and to identify and characterize key farm types. The survey was based on farm household characteristics, farm structure, farming practices and livestock as well as the economic performance of the farm. Principal component analysis (PCA) and cluster analysis (CA) were used to establish the different farm typologies, and the data set based on 27 variables was carefully analysed. The findings confirmed that the key variables that significantly affect the adoption of new agricultural technologies relate to age, farming experience, level of education of the household head, income, access to markets, land ownership, the proportion of hired labour, savings, food self-sufficiency and income from off-farm activities. Four main farm types were identified in the study area based on resource endowment and livelihood orientation. These are (1) well-resourced farmers entirely dependent on agriculture and less reliant on off-farm activities; (2) moderately resourced households, which are headed by an older male with greater farming experience and which are engaged in both on-farm and off-farm activities; (3) resource-constrained households with cattle as the main livestock and with income generated by the sale of livestock products; and (4) severely resource-constrained households which are headed by young farmers/men and where income is generated by off-farm activities. These four farm categories represent the heterogeneity of farms in North-West Bangladesh, and it is hoped that the development of this farm household typology will help particularly the extension service, to set up appropriate extension advice that will benefit the farming community.

Climate-Smart Agriculture (CSA) has had an increasing role in the agricultural policy arena, as it aims to address climate change mitigation, adaptation and food security goals in an integrated way. In regions where agriculture has been constrained because of soil degradation and climate change, CSA aims to implement soil-based strategies that restore soil function and increase carbon storage. The extent to which such strategies succeed in achieving mitigation, adaptation and productivity goals is referred to as climate-smartness. The co-evolution of yield and Soil Organic Carbon (SOC) over the years presents a proxy for the trade-off between productivity, soil fertility and carbon sequestration. Yield and SOC are widely monitored, analysed and used to inform CSA planning. Yet their analysis is often conducted separately and for a small number of years, which neglects long-term soil fertility dynamics and their impact on crops. Given the absence of integrated climate-smartness metrics to capture the trade-offs and synergies between SOC and yield, we present a soil-based Climate-Smartness Index (SCSI). The SCSI is computed using normalized indicators of trend and variability of annual changes on yield and SOC data. The SCSI was calculated for a set of published experiments that compared Conservation Agriculture (CA) practices with conventional management. The CA treatments scored higher SCSI during the first 5 years of evaluation as compared to conventional management. Analysis of the temporal dynamics of climate-smartness indicated that minimum SCSI values typically occurred before 5 years after the start of the experiment, indicating potential trade-offs between SOC and yield. Conversely, SCSI values peaked between 5 and 10 years. After 20 years, the SCSI tended towards zero, as substantial changes in either SOC or yield are no longer evidenced. The SCSI can be calculated for annual crops under any soil management and at different time periods, providing a consistent metric for climate-smartness across both practices and time.

Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper., Highlights • 14 criteria for use of crop models in assessments of impacts, adaptation and risk • Working with stakeholders to identify timing of risks is key to risk assessments. • Multiple methods needed to critically assess the use of climate model output • Increasing transparency and inter-comparability needed in risk assessments

Since 1990 the Intergovernmental Panel on Climate Change (IPCC) has produced five Assessment Reports (ARs), in which agriculture as the production of food for humans via crops and livestock have featured in one form or another. A constructed data base of the ca. 2,100 cited experiments and simulations in the five ARs were analysed with respect to impacts on yields via crop type, region and whether or not adaptation was included. Quantitative data on impacts and adaptation in livestock farming have been extremely scarce in the ARs. The main conclusions from impact and adaptation are that crop yields will decline but that responses have large statistical variation. Mitigation assessments in the ARs have used both bottom-up and top-down methods but need better to link emissions and their mitigation with food production and security. Relevant policy options have become broader in later ARs and included more of the social and non-production aspects of food security. Our overall conclusion is that agriculture and food security, which are two of the most central, critical and imminent issues in climate change, have been dealt with in an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modelling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and non-production aspects of food security in future roles for integrated assessment models.

A warming climate will affect regional precipitation and hence food supply. However, only a few regions around the world are currently undergoing precipitation changes that can be attributed to climate change. Knowing when such changes are projected to emerge outside natural variability—the time of emergence (TOE)—is critical for taking effective adaptation measures. Using ensemble climate projections, we determine the TOE of regional precipitation changes globally and in particular for the growing areas of four major crops. We find relatively early (

The potato cyst nematodes Globodera pallida and G. rostochiensis are economically important plant pathogens causing losses to UK potato harvests estimated at £50 m/ year. Implications of climate change on their future pest status have not been fully considered. Here, we report growth of female G. pallida and G. rostochiensis over the range 15 to 25°C. Females per plant and their fecundity declined progressively with temperatures above 17.5°C for G. pallida, whilst females per plant were optimal between 17.5 and 22.5°C for G. rostochiensis. Relative reproductive success with temperature was confirmed on two potato cultivars infected with either species at 15, 22.5 and 25°C. The reduced reproductive success of G. pallida at 22.5°C relative to 15°C was also recorded for a further seven host cultivars studied. The differences in optimal temperatures for reproductive success may relate to known differences in the altitude of their regions of origin in the Andes. Exposure of G. pallida to a diurnal temperature stress for one week during female growth significantly suppressed subsequent growth for one week at 17.5°C but had no effect on G. rostochiensis. However, after two weeks of recovery, female size was not significantly different from that for the control treatment. Future soil temperatures were simulated for medium‐ and high‐emission scenarios and combined with nematode growth data to project future implications of climate change for the two species. Increased soil temperatures associated with climate change may reduce the pest status of G. pallida but benefit G. rostochiensis especially in the southern United Kingdom. We conclude that plant breeders may be able to exploit the thermal limits of G. pallida by developing potato cultivars able to grow under future warm summer conditions. Existing widely deployed resistance to G. rostochiensis is an important characteristic to retain for new potato cultivars.

Crop models are imperfect approximations to real world interactions between biotic and abiotic factors. In some situations, the uncertainties associated with choices in model structure, model inputs and parameters can exceed the spatiotemporal variability of simulated yields, thus limiting predictability. For Indian groundnut, we used the General Large Area Model for annual crops (GLAM) with an existing framework to decompose uncertainty, to first understand how skill changes with added model complexity, and then to determine the relevant uncertainty sources in yield and other prognostic variables (total biomass, leaf area index and harvest index). We developed an ensemble of simulations by perturbing GLAM parameters using two different input meteorology datasets, and two model versions that differ in the complexity with which they account for assimilation. We found that added complexity improved model skill, as measured by changes in the root mean squared error (RMSE), by 5-10% in specific pockets of western, central and southern India, but that 85% of the groundnut growing area either did not show improved skill or showed decreased skill from such added complexity. Thus, adding complexity or using overly complex models at regional or global scales should be exercised with caution. Uncertainty analysis indicated that, in situations where soil and air moisture dynamics are the major determinants of productivity, predictability in yield is high. Where uncertainty for yield is high, the choice of weather input data was found critical for reducing uncertainty. However, for other prognostic variables (including leaf area index, total biomass and the harvest index) parametric uncertainty was generally the most important source, with a contribution of up to 90% in some cases, suggesting that regional-scale data additional to yield to constrain model parameters is needed. Our study provides further evidence that regional-scale studies should explicitly quantify multiple uncertainty sources.

The interior savannah ecosystem in Ghana is subjected to a number of hazards, including droughts, windstorms, high temperatures and heavy rainfall, the frequency and intensity of which are projected to increase during the 21st century as a result of climate variability and change. Vulnerabilities to these hazards vary, both spatially and temporally, due to differences in susceptibilities and adaptive capacities. Many mapping exercises in Ghana have considered the impacts of single hazards on single sectors, particularly agriculture. But the hazards often occur concurrently or alternately, and have varying degrees of impacts on different sectors. The impacts also interact. These interactions make mapping of the vulnerabilities of multiple sectors to multiple hazards imperative. This paper presents an analysis of the spatial dimension of vulnerabilities by mapping vulnerability of sectors that support livelihood activities at a single point in time, using the Upper East Region of Ghana as a case study. Data colected to develop the maps were largely quantitative and from secondary sources. Other data drew on fieldwork undertaken in the region from July - September 2013. Quantitative values were assigned to qualitative categorical data as the mapping process is necessarily quantitative. Data were divided into susceptibility and adaptive capacity indicators and mapped in ArcGIS 10.2 using weighted linear sum aggregation. Agriculture was found to be the most vulnerable sector in all districts of the Upper East Region and experienced the greatest shocks from all hazards. Although all districts were vulnerable, the Talensi, Nabdam, Garu-Temapane and Kassena-Nankana West Districts were most vulnerable. Findings highlight the need for more targeted interventions to build adaptive capacity in light of the spatial distributions of vulnerabilities to hazards across sectors.

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

The development of crop varieties that are better suited to new climatic conditions is vital for future food production1, 2. Increases in mean temperature accelerate crop development, resulting in shorter crop durations and reduced time to accumulate biomass and yield3, 4. The process of breeding, delivery and adoption (BDA) of new maize varieties can take up to 30 years. Here, we assess for the first time the implications of warming during the BDA process by using five bias-corrected global climate models and four representative concentration pathways with realistic scenarios of maize BDA times in Africa. The results show that the projected difference in temperature between the start and end of the maize BDA cycle results in shorter crop durations that are outside current variability. Both adaptation and mitigation can reduce duration loss. In particular, climate projections have the potential to provide target elevated temperatures for breeding. Whilst options for reducing BDA time are highly context dependent, common threads include improved recording and sharing of data across regions for the whole BDA cycle, streamlining of regulation, and capacity building. Finally, we show that the results have implications for maize across the tropics, where similar shortening of duration is projected.

Climate change is projected to constitute a significant threat to food security if no adaptation actions are taken. Transformation of agricultural systems, for example switching crop types or moving out of agriculture, is projected to be necessary in some cases. However, little attention has been paid to the timing of these transformations. Here, we develop a temporal uncertainty framework using the CMIP5 ensemble to assess when and where cultivation of key crops in sub-Saharan Africa becomes unviable. We report potential transformational changes for all major crops during the twenty-first century, as climates shift and areas become unsuitable. For most crops, however, transformation is limited to small pockets (

International audience; The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

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.

Insect pollinators of crops and wild plants are under threat globally and their decline or loss could have profound economic and environmental consequences. Here, we argue that multiple anthropogenic pressures – including land-use intensification, climate change, and the spread of alien species and diseases – are primarily responsible for insect-pollinator declines. We show that a complex interplay between pressures (eg lack of food sources, diseases, and pesticides) and biological processes (eg species dispersal and interactions) at a range of scales (from genes to ecosystems) underpins the general decline in insect-pollinator populations. Interdisciplinary research on the nature and impacts of these interactions will be needed if human food security and ecosystem function are to be preserved. We highlight key areas that require research focus and outline some practical steps to alleviate the pressures on pollinators and the pollination services they deliver to wild and crop plants.