Emergence of robust precipitation changes across crop production areas in the 21st century.

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 (<2040) emergence of precipitation trends for all four crops. Reduced (increased) precipitation trends encompass 1-14% (3-31%) of global production of maize, wheat, rice, and soybean. Comparing results for RCP8.5 and RCP2.6 clearly shows that emissions compatible with the Paris Agreement result in far less cropped land experiencing novel climates. However, the existence of a TOE, even under the lowest emission scenario, and a small probability for early emergence emphasize the urgent need for adaptation measures. We also show how both the urgency of adaptation and the extent of mitigation vary geographically.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2 ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2 ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2 ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Invited review: Intergovernmental Panel on Climate Change, agriculture, and food-A case of shifting cultivation and history.

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 database of the ca. 2,100 cited experiments and simulations in the five ARs was analyzed with respect to impacts on yields via crop type, region, and whether 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 nonproduction 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 an unfocussed and inconsistent manner between the IPCC five ARs. This is partly a result of not only agriculture spanning two IPCC working groups but also the very strong focus on projections from computer crop simulation modeling. For the future, we suggest a need to examine interactions between themes such as crop resource use efficiencies and to include all production and nonproduction aspects of food security in future roles for integrated assessment models.

Climate change impact and adaptation for wheat protein.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production.

Breeding implications of drought stress under future climate for upland rice in Brazil.

Rice is the most important food crop in the developing world. For rice production systems to address the challenges of increasing demand and climate change, potential and on-farm yield increases must be increased. Breeding is one of the main strategies toward such aim. Here, we hypothesize that climatic and atmospheric changes for the upland rice growing period in central Brazil are likely to alter environment groupings and drought stress patterns by 2050, leading to changing breeding targets during the 21st century. As a result of changes in drought stress frequency and intensity, we found reductions in productivity in the range of 200-600 kg/ha (up to 20%) and reductions in yield stability throughout virtually the entire upland rice growing area (except for the southeast). In the face of these changes, our crop simulation analysis suggests that the current strategy of the breeding program, which aims at achieving wide adaptation, should be adjusted. Based on the results for current and future climates, a weighted selection strategy for the three environmental groups that characterize the region is suggested. For the highly favorable environment (HFE, 36%-41% growing area, depending on RCP), selection should be done under both stress-free and terminal stress conditions; for the favorable environment (FE, 27%-40%), selection should aim at testing under reproductive and terminal stress, and for the least favorable environment (LFE, 23%-27%), selection should be conducted for response to reproductive stress only and for the joint occurrence of reproductive and terminal stress. Even though there are differences in timing, it is noteworthy that stress levels are similar across environments, with 40%-60% of crop water demand unsatisfied. Efficient crop improvement targeted toward adaptive traits for drought tolerance will enhance upland rice crop system resilience under climate change.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Improving the use of crop models for risk assessment and climate change adaptation.

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.

Integrating Plant Science and Crop Modeling: Assessment of the Impact of Climate Change on Soybean and Maize Production.

Increasing global CO2 emissions have profound consequences for plant biology, not least because of direct influences on carbon gain. However, much remains uncertain regarding how our major crops will respond to a future high CO2 world. Crop model inter-comparison studies have identified large uncertainties and biases associated with climate change. The need to quantify uncertainty has drawn the fields of plant molecular physiology, crop breeding and biology, and climate change modeling closer together. Comparing data from different models that have been used to assess the potential climate change impacts on soybean and maize production, future yield losses have been predicted for both major crops. When CO2 fertilization effects are taken into account significant yield gains are predicted for soybean, together with a shift in global production from the Southern to the Northern hemisphere. Maize production is also forecast to shift northwards. However, unless plant breeders are able to produce new hybrids with improved traits, the forecasted yield losses for maize will only be mitigated by agro-management adaptations. In addition, the increasing demands of a growing world population will require larger areas of marginal land to be used for maize and soybean production. We summarize the outputs of crop models, together with mitigation options for decreasing the negative impacts of climate on the global maize and soybean production, providing an overview of projected land-use change as a major determining factor for future global crop production.

Climate change is predicted to alter the current pest status of Globodera pallida and G. rostochiensis in the United Kingdom.

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.

Potential negative consequences of geoengineering on crop production: A study of Indian groundnut.

Geoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop-climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased.

Addressing uncertainty in adaptation planning for agriculture.

We present a framework for prioritizing adaptation approaches at a range of timeframes. The framework is illustrated by four case studies from developing countries, each with associated characterization of uncertainty. Two cases on near-term adaptation planning in Sri Lanka and on stakeholder scenario exercises in East Africa show how the relative utility of capacity vs. impact approaches to adaptation planning differ with level of uncertainty and associated lead time. An additional two cases demonstrate that it is possible to identify uncertainties that are relevant to decision making in specific timeframes and circumstances. The case on coffee in Latin America identifies altitudinal thresholds at which incremental vs. transformative adaptation pathways are robust options. The final case uses three crop-climate simulation studies to demonstrate how uncertainty can be characterized at different time horizons to discriminate where robust adaptation options are possible. We find that impact approaches, which use predictive models, are increasingly useful over longer lead times and at higher levels of greenhouse gas emissions. We also find that extreme events are important in determining predictability across a broad range of timescales. The results demonstrate the potential for robust knowledge and actions in the face of uncertainty.

Identifying traits for genotypic adaptation using crop models.

Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change; and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.

Crop yield response to climate change varies with cropping intensity.

Projections of the response of crop yield to climate change at different spatial scales are known to vary. However, understanding of the causes of systematic differences across scale is limited. Here, we hypothesize that heterogeneous cropping intensity is one source of scale dependency. Analysis of observed global data and regional crop modelling demonstrate that areas of high vs. low cropping intensity can have systematically different yields, in both observations and simulations. Analysis of global crop data suggests that heterogeneity in cropping intensity is a likely source of scale dependency for a number of crops across the globe. Further crop modelling and a meta-analysis of projected tropical maize yields are used to assess the implications for climate change assessments. The results show that scale dependency is a potential source of systematic bias. We conclude that spatially comprehensive assessments of climate impacts based on yield alone, without accounting for cropping intensity, are prone to systematic overestimation of climate impacts. The findings therefore suggest a need for greater attention to crop suitability and land use change when assessing the impacts of climate change.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Climate variability and vulnerability to climate change: a review.

The focus of the great majority of climate change impact studies is on changes in mean climate. In terms of climate model output, these changes are more robust than changes in climate variability. By concentrating on changes in climate means, the full impacts of climate change on biological and human systems are probably being seriously underestimated. Here, we briefly review the possible impacts of changes in climate variability and the frequency of extreme events on biological and food systems, with a focus on the developing world. We present new analysis that tentatively links increases in climate variability with increasing food insecurity in the future. We consider the ways in which people deal with climate variability and extremes and how they may adapt in the future. Key knowledge and data gaps are highlighted. These include the timing and interactions of different climatic stresses on plant growth and development, particularly at higher temperatures, and the impacts on crops, livestock and farming systems of changes in climate variability and extreme events on pest-weed-disease complexes. We highlight the need to reframe research questions in such a way that they can provide decision makers throughout the food system with actionable answers, and the need for investment in climate and environmental monitoring. Improved understanding of the full range of impacts of climate change on biological and food systems is a critical step in being able to address effectively the effects of climate variability and extreme events on human vulnerability and food security, particularly in agriculturally based developing countries facing the challenge of having to feed rapidly growing populations in the coming decades.

Climate-driven spatial mismatches between British orchards and their pollinators: increased risks of pollination deficits.

Understanding how climate change can affect crop-pollinator systems helps predict potential geographical mismatches between a crop and its pollinators, and therefore identify areas vulnerable to loss of pollination services. We examined the distribution of orchard species (apples, pears, plums and other top fruits) and their pollinators in Great Britain, for present and future climatic conditions projected for 2050 under the SRES A1B Emissions Scenario. We used a relative index of pollinator availability as a proxy for pollination service. At present, there is a large spatial overlap between orchards and their pollinators, but predictions for 2050 revealed that the most suitable areas for orchards corresponded to low pollinator availability. However, we found that pollinator availability may persist in areas currently used for fruit production, which are predicted to provide suboptimal environmental suitability for orchard species in the future. Our results may be used to identify mitigation options to safeguard orchard production against the risk of pollination failure in Great Britain over the next 50 years; for instance, choosing fruit tree varieties that are adapted to future climatic conditions, or boosting wild pollinators through improving landscape resources. Our approach can be readily applied to other regions and crop systems, and expanded to include different climatic scenarios.

Climate change enhances stability of wheat-flowering-date.

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Stakeholder-driven transformative adaptation is needed for climate-smart nutrition security in sub-Saharan Africa.

Improving nutrition security in sub-Saharan Africa under increasing climate risks and population growth requires a strong and contextualized evidence base. Yet, to date, few studies have assessed climate-smart agriculture and nutrition security simultaneously. Here we use an integrated assessment framework (iFEED) to explore stakeholder-driven scenarios of food system transformation towards climate-smart nutrition security in Malawi, South Africa, Tanzania and Zambia. iFEED translates climate-food-emissions modelling into policy-relevant information using model output implication statements. Results show that diversifying agricultural production towards more micronutrient-rich foods is necessary to achieve an adequate population-level nutrient supply by mid-century. Agricultural areas must expand unless unprecedented rapid yield improvements are achieved. While these transformations are challenging to accomplish and often associated with increased greenhouse gas emissions, the alternative for a nutrition-secure future is to rely increasingly on imports, which would outsource emissions and be economically and politically challenging given the large import increases required.

Effects of combined abiotic stresses on nutrient content of European wheat and implications for nutritional security under climate change.

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.

Enhanced Leaf Cooling Is a Pathway to Heat Tolerance in Common Bean.

Common bean is the most consumed legume in the world and an important source of protein in Latin America, Eastern, and Southern Africa. It is grown in a variety of environments with mean air temperatures of between 14°C and 35°C and is more sensitive to high temperatures than other legumes. As global heating continues, breeding for heat tolerance in common bean is an urgent priority. Transpirational cooling has been shown to be an important mechanism for heat avoidance in many crops, and leaf cooling traits have been used to breed for both drought and heat tolerance. As yet, little is known about the magnitude of leaf cooling in common bean, nor whether this trait is functionally linked to heat tolerance. Accordingly, we explore the extent and genotypic variation of transpirational cooling in common bean. Our results show that leaf cooling is an important heat avoidance mechanism in common bean. On average, leaf temperatures are 5°C cooler than air temperatures, and can range from between 13°C cooler and 2°C warmer. We show that the magnitude of leaf cooling keeps leaf temperatures within a photosynthetically functional range. Heat tolerant genotypes cool more than heat sensitive genotypes and the magnitude of this difference increases at elevated temperatures. Furthermore, we find that differences in leaf cooling are largest at the top of the canopy where determinate bush beans are most sensitive to the impact of high temperatures during the flowering period. Our results suggest that heat tolerant genotypes cool more than heat sensitive genotypes as a result of higher stomatal conductance and enhanced transpirational cooling. We demonstrate that it is possible to accurately simulate the temperature of the leaf by genotype using only air temperature and relative humidity. Our work suggests that greater leaf cooling is a pathway to heat tolerance. Bean breeders can use the difference between air and leaf temperature to screen for genotypes with enhanced capacity for heat avoidance. Once evaluated for a particular target population of environments, breeders can use our model for modeling leaf temperatures by genotype to assess the value of selecting for cooler beans.