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.
Crop breeding for resilience to changing climates is a key area of investment in African agricultural development, but proactively breeding for uncertain future climates is challenging. In this ...paper, we characterise efforts to breed new varieties of crops for climate resilience in southern Africa and evaluate the extent to which climate model projections currently inform crop breeding activity. Based on a survey of seed system actors, we find that the prioritisation of crops and traits is only informed to a limited extent by modelled projections. We use an ensemble of CORDEX models for mid and end of century for southern Africa to test some of the assumptions that underpin current breeding activity, particularly associated with breeding for reduced durations and drought tolerance in maize, and demonstrate some of the ways in which such projections can help to inform breeding priorities and agenda setting (e.g. through the case of assessing cassava toxicity risk). Based on these examples, we propose five potential applications of climate models in informing breeding priorities. Furthermore, after unpacking the sources of uncertainty within the presented model projections, we discuss general principles for the appropriate use of climate model information in crop breeding.
Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to ...study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.
Abstract
Machine learning (ML) is the most advanced field of predictive modelling and incorporating it into process-based crop modelling is a highly promising avenue for accurate predictions of plant ...growth, development and yield. Here, we embed ML algorithms into a process-based crop model. ML is used within GLAM-Parti for daily predictions of radiation use efficiency, the rate of change of harvest index and the days to anthesis and maturity. The GLAM-Parti-ML framework exhibited high skill for wheat growth and development in a wide range of temperature, solar radiation and atmospheric humidity conditions, including various levels of heat stress. The model exhibited less than 20 % error in simulating the above-ground biomass, grain yield and the days to anthesis and maturity of three wheat cultivars in six countries (USA, Mexico, Egypt, India, the Sudan and Bangladesh). Moreover, GLAM-Parti reproduced around three-quarters of the observed variance in wheat biomass and yield. Existing process-based crop models rely on empirical stress factors to limit growth potential in simulations of crop response to unfavourable environmental conditions. The incorporation of ML into GLAM-Parti eliminated all stress factors under high-temperature environments and reduced the physiological model parameters down to four. We conclude that the combination of process-based crop modelling with the predictive capacity of ML makes GLAM-Parti a highly promising framework for the next generation of crop models.
Current-climate precipitation and temperature extremes have been identified by decision makers in West Africa as among the more impactful weather events causing lasting socioeconomic damage. In this ...article, we use a plausible future-climate scenario (RCP8.5) for the end of the twenty-first century to explore the relative commonness of such extremes under global warming. The analysis presented considers what a typical day in the future climate will feel like relative to current extrema. Across much of West Africa, we see that the typical future-climate day has maximum and minimum temperatures greater than 99.5% of currently experienced values. This finding exists for most months but is particularly pronounced during the Boreal spring and summer. The typical future precipitation event has a daily rainfall rate greater than 95% of current storms. These findings exist in both a future scenario model run with and without parameterised convection, and for many of the Coupled Model Inter-comparison Project version 5 ensemble members. Additionally, agronomic monsoon onset is projected to occur later and have greater inter-annual variability in the future. Our findings suggest far more extreme conditions in future climate over West Africa. The projected changes in temperature and precipitation could have serious socioeconomic implications, stressing the need for effective mitigation given the potential lack of adaptation pathways available to decision makers.
The contribution of potatoes to the global food supply is increasing—consumption more than doubled in developing countries between 1960 and 2005. Understanding climate change impacts on global potato ...yields is therefore important for future food security. Analyses of climate change impacts on potato compared to other major crops are rare, especially at the global scale. Of two global gridded potato modeling studies published at the time of this analysis, one simulated the impacts of temperature increases on potential potato yields; the other did not simulate the impacts of farmer adaptation to climate change, which may offset negative climate change impacts on yield. These studies may therefore overestimate negative climate change impacts on yields as they do not simultaneously include CO
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fertilisation and adaptation to climate change. Here we simulate the abiotic impacts of climate change on potato to 2050 using the GLAM crop model and the ISI-MIP ensemble of global climate models. Simulations include adaptations to climate change through varying planting windows and varieties and CO
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fertilisation, unlike previous global potato modeling studies. Results show significant skill in reproducing observed national scale yields in Europe. Elsewhere, correlations are generally positive but low, primarily due to poor relationships between national scale observed yields and climate. Future climate simulations including adaptation to climate change through changing planting windows and crop varieties show that yields are expected to increase in most cases as a result of longer growing seasons and CO
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fertilisation. Average global yield increases range from 9 to 20% when including adaptation. The global average yield benefits of adaptation to climate change range from 10 to 17% across climate models. Potato agriculture is associated with lower green house gas emissions relative to other major crops and therefore can be seen as a climate smart option given projected yield increases with adaptation.
In our study, we analyzed 30years of climatological data revealing the bean production risks for Western Amazonia. Climatological profiling showed high daytime and nighttime temperatures combined ...with high relative humidity and low vapor pressure deficit. Our understanding of the target environment allows us to select trait combinations for reaching higher yields in Amazonian acid soils. Our research was conducted using 64 bean lines with different genetic backgrounds. In high temperatures, we identified three water use efficiency typologies in beans based on detailed data analysis on gasometric exchange. Profligate water spenders and not water conservative accessions showed leaf cooling, and effective photosynthate partitioning to seeds, and these attributes were found to be related to higher photosynthetic efficiency. Thus, water spenders and not savers were recognized as heat resistant in acid soil conditions in Western Amazonia. Genotypes such as BFS 10, SEN 52, SER 323, different SEFs (SEF 73, SEF 10, SEF 40, SEF 70), SCR 56, SMR 173, and SMN 99 presented less negative effects of heat stress on yield. These genotypes could be suitable as parental lines for improving dry seed production. The improved knowledge on water-use efficiency typologies can be used for bean crop improvement efforts as well as further studies aimed at a better understanding of the intrinsic mechanisms of heat resistance in legumes.
Sowing and harvest dates are a significant source of uncertainty within crop models, especially for regions where high-resolution data are unavailable or, as is the case in future climate runs, where ...no data are available at all. Global datasets are not always able to distinguish when wheat is grown in tropical and subtropical regions, and they are also often coarse in resolution. South Asia is one such region where large spatial variation means higher-resolution datasets are needed, together with greater clarity for the timing of the main wheat growing season. Agriculture in South Asia is closely associated with the dominating climatological phenomenon, the Asian summer monsoon (ASM). Rice and wheat are two highly important crops for the region, with rice being mainly cultivated in the wet season during the summer monsoon months and wheat during the dry winter. We present a method for estimating the crop sowing and harvest dates for rice and wheat using the ASM onset and retreat. The aim of this method is to provide a more accurate alternative to the global datasets of cropping calendars than is currently available and generate more representative inputs for climate impact assessments.
Societal Impact Statement
As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a ...changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate adaptation.
Summary
There is an urgent need to accelerate crop breeding for adaptation to a changing climate. As the growing season changes, crop improvement programmes must ensure that the phenological characteristics of the varieties they develop remain well suited to their target population of environments.
Meeting this challenge will require a clear understanding of how existing germplasm behave across Genotype ∗ Environment (G ∗ E) to enhance the efficiency of selection. Recent work calls for the development of simple models that can accurately simulate genotypic variation in key traits across target population of environments.
Accordingly, we develop a simple machine learning framework for modelling time to flowering across G ∗ E and apply this to common bean in an equatorial target population of environments. Within this framework, we test three machine learning models and find that the best performing models display high levels of accuracy across G ∗ E.
We advance understanding of the environmental drivers of flowering time in equatorial conditions by showing that thermal time and accumulated evaporation are powerful predictors of flowering time across all three models.
As the growing season changes, the development of climate resilient crop varieties has emerged as a crucial adaptation in agricultural systems. Breeding new varieties for a changing climate requires enhanced capacity to predict the complex interactions between genotype and environment that determine flowering time. Hundreds of experiments with observations of flowering, the environment and plant genetics were used to build a model that can predict when a variety of common bean is going to flower. This model will help breeders to explore the phenological characteristics of their germplasm, speeding up selection for climate 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.
•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
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