Statistical Crop Models Research Articles

Improved Winter Wheat Yield Estimation by Combining Remote Sensing Data, Machine Learning, and Phenological Metrics

Accurate yield prediction is essential for global food security and effective agricultural management. Traditional empirical statistical models and crop models face significant limitations, including high computational demands and dependency on high-resolution soil and daily weather data, that restrict their scalability across different temporal and spatial scales. Moreover, the lack of sufficient observational data further hinders the broad application of these methods. In this study, building on the SCYM method, we propose an integrated framework that combines crop models and machine learning techniques to optimize crop yield modeling methods and the selection of vegetation indices. We evaluated three commonly used vegetation indices and three widely applied ML techniques. Additionally, we assessed the impact of combining meteorological and phenological variables on yield estimation accuracy. The results indicated that the green chlorophyll vegetation index (GCVI) outperformed the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) in linear models, achieving an R2 of 0.31 and an RMSE of 396 kg/ha. Non-linear ML methods, particularly LightGBM, demonstrated superior performance, with an R2 of 0.42 and RMSE of 365 kg/ha for GCVI. The combination of GCVI with meteorological and phenological data provided the best results, with an R2 of 0.60 and an RMSE of 295 kg/ha. Our proposed framework significantly enhances the accuracy and efficiency of winter wheat yield estimation, supporting more effective agricultural management and policymaking.

Wheat yields in Kazakhstan can successfully be forecasted using a statistical crop model

Wheat production in Kazakhstan is fundamentally contributing to food security in Central Asia and beyond. It gained even more importance after recent spikes in global food prices in 2022. Therefore, timely and reliable estimates of Kazakh wheat production are important for food security planning and management. In this study, we developed a statistical weather-driven crop model that can successfully hindcast wheat yields at the oblast level up to two months before the harvest. The hindcast of wheat yields for 1993–2021 produces a median R2 of 0.69 for the full model run and R2 values of 0.60 and 0.37 for two levels of out-of-sample validations, respectively. Based on these yield estimates we provide a robust hindcast of the total wheat production for Kazakhstan with R2 values between 0.86 and 0.73. We forecast total wheat production in Kazakhstan for 2022 to be 12.4 million tonnes and the average yield to be 0.96 tonnes per hectare, which is 5 % above the production and yield of 2021 (assuming equal areas). The statistical model is run with publicly available weather and yield data and requires low computational power, making it easily replicable. The forecast model can be used as a replenishment to currently applied forecasting methods supporting countries in Central Asia to meet their food demand.

Comprehensive Impacts of Climate Change on Rice Production and Adaptive Strategies in China.

The rice production system is one of the most climate change sensitive agro-ecosystems. This paper reviews the effects of current and future climate change on rice production in China. In recent decades, thermal resources have increased during the rice growing season, while solar radiation resources have decreased, and precipitation heterogeneity has increased. The increasing frequency of high-temperature stress, heavy rainfall, drought, and flood disasters may reduce the utilization efficiency of hydrothermal resources. Climate change, thus far, has resulted in a significant northward shift in the potential planting boundaries of single- and double-cropping rice production systems, which negatively affects the growth duration of single-, early-, and late-cropping rice. Studies based on statistical and process-based crop models show that climate change has affected rice production in China. The effects of climate change on the yield of single rice (SR), early rice (ER), and late rice (LR) were significant; however, the results of different methods and different rice growing areas were different to some extent. The trend of a longer growth period and higher yield of rice reflects the ability of China’s rice production system to adapt to climate change by adjusting planting regionalization and improving varieties and cultivation techniques. The results of the impact assessment under different climate scenarios indicated that the rice growth period would shorten and yield would decrease in the future. This means that climate change will seriously affect China’s rice production and food security. Further research requires a deeper understanding of abiotic stress physiology and its integration into ecophysiological models to reduce the uncertainty of impact assessment and expand the systematicness of impact assessment.

Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models

Temperature impacts on crop yield are known to be dependent on concurrent precipitation conditions and vice versa. To date, their confounding effects, as well as the associated uncertainties, are not well quantified at the global scale. Here, we disentangle the separate and confounding effects of temperature and precipitation on global maize yield under 25 climate scenarios. Instead of relying on a single type of crop model, as pursued in most previous impact assessments, we utilize machine learning, statistical and process-based crop models in a novel approach that allows for reasonable inter-method comparisons and uncertainty quantifications. Through controlling precipitation, an increase in warming of 1 °C could cause a global yield loss of 6.88%, 4.86% or 5.61% according to polynomial regression, long short-term memory (LSTM) and process-based crop models, respectively. With a 10% increase in precipitation, such negative temperature effects could be mitigated by 3.98%, 1.05% or 3.10%, respectively. When temperature is fixed at the baseline level, a 10% increase in precipitation alone could lead to a global yield growth of 0.23%, 1.43% or 3.09% according to polynomial regression, LSTM and process-based crop models, respectively. Further analysis demonstrates substantial uncertainties in impact assessment across crop models, which show a larger discrepancy in predicting temperature impacts than precipitation effects. Overall, global-scale assessment is more uncertain under drier conditions than under wet conditions, while a diverse uncertainty pattern is found for the top ten maize producing countries. This study highlights the important role of climate interactions in regulating yield response to changes in a specific climate factor and emphasizes the value of using both machine learning, statistical and process crop models in a consistent manner for a more realistic estimate of uncertainty than would be provided by a single type of model.

A forecast of staple crop production in Burkina Faso to enable early warnings of shortages in domestic food availability

Almost half of the Burkinabe population is moderately or severely affected by food insecurity. With climate change, domestic food production may become more under pressure, further jeopardizing food security. In this study, we focus on the production of maize, sorghum and millet as staple cereal crops in Burkina Faso to assess food availability as one component of food security. Based on a statistical weather-driven crop model, we provide a within-season forecast of crop production 1 month before the harvest. Hindcast results from 1984 to 2018 produce an r2 of 0.95 in case of known harvest areas and an r2 of 0.88 when harvest areas are modelled instead. We compare actually supplied calories with those usually consumed from staple crops, allowing us to provide early information on shortages in domestic cereal production on the national level. Despite the—on average—sufficient domestic cereal production from maize, sorghum and millet, a considerable level of food insecurity prevails for large parts of the population. We suggest to consider such forecasts as an early warning signal for shortages in domestic staple crop production and encourage a comprehensive assessment of all dimensions of food security to rapidly develop counteractions for looming food crises.

Economic impacts of climate-induced crop yield changes: evidence from agri-food industries in six countries

The potential impact of climate change on agriculture has been one of the most discussed topics in the literature on climate change. Although the possible impacts of climate change on crop yields have been widely studied, there remains little quantitative understanding of the heterogeneous economic responses to climate-induced crop yield changes in different economies, particularly at higher levels of warming. This study assesses the economic impacts of eight scenarios of warming, from 1.5 to 4 °C, on rice and wheat yields in China, India, Brazil, Egypt, Ghana and Ethiopia. The role of both natural and social factors in crop production is considered by coupling a statistical crop model (ClimaCrop) and a global economic model (GTAP). Changes in economic outputs, consumer and producer prices and national economic welfare are presented. The study shows marginal benefits of crop yield changes on GDP and welfare in China up to 3.5 and 3.0 °C, respectively. This is due to projected increases in rice yields which lower domestic consumer rice prices. Although at higher warming levels these trends begin to reverse. The other countries are negatively impacted due to declining crop yields, with increasing consumer prices of domestic and imported rice and wheat. GDP and welfare declines, with more severe reductions associated with the higher warming levels, particularly in India and Ethiopia. The method is beneficial as the economic outputs reflect a more in-depth picture of the response of global markets and ultimately regional consequences of agricultural impacts that will be of importance to decision makers.

Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

Assessing the Impacts of Climate Change and Variability on Maize (Zea mays) Yield over Tanzania

This study aimed at understanding the impacts of the seasonal hydroclimatic variables on maize yield and developing of statistical crop model for future maize yield prediction over Tanzania. The food security of the country is basically determined by availability of maize. Unfortunately, agriculture over the country is mainly rain fed hence highly endangered by the detrimental consequences of climate change and variability. Observed climate data was acquired from Tanzania Meteorological Authority (TMA) and Maize yield data from Food and Agriculture Organization (FAO). The study used the Mann-Kendall test and Sen’s slope for trend and magnitude detection in minimum, maximum temperature and rainfall at the 95% confidence level. The results have shown that rainfall is decreasing over the country and especially during the growing season but increasing during short rains season. Characteristics of seasonal climatic variables, cycle during growing period were linked to maize yield, and high (low) yield was reported during anomalous wet (dry) growing seasons. This portrays seasonal dependence of maize production. Statistical crop model was built by aggregating spatial regions that have statistically significant relation with maize yield. Results show that, 58.8% of yield variance is linked to seasonal hydroclimate variability. Rainfall emerged as the dominant predictor variable for maize yield since it accounts for 44.1% of yield variance. The modeled and observed yields exhibit statistically substantial relationship (r = 0.78) hence depicting high credence of the built statistical crop model. Also, the results revealed a decreasing trend in Maize yield with further Lessing trend is projected to proceed in the future. This calls for adaptation and implementation of appropriate regional measures to raise maize production in order to feed the burgeoning human population amidst climate change.

Using a Statistical Crop Model to Predict Maize Yield by the End-Of-Century for the Azuero Region in Panama

In this article, we evaluate the impact of temperature and precipitation at the end of the 21st century (2075–2099) on the yield of maize in the Azuero Region in Panama. Using projected data from an atmospheric climate model, MRI-ACGM 3.2S, the study variables are related to maize yield (t ha−1) under four different sea surface Temperature (SST) Ensembles (C0, C1, C2, and C3) and in three different planting dates (21 August, 23 September, and 23 October). In terms climate, results confirm the increase in temperatures and precipitation intensity that has been projected for the region at the end of the century. Moreover, differences are found in the average precipitation patterns of each SST-ensemble, which leads to difference in maize yield. SST-Ensembles C0, C1, and C3 predict a doubling of the yield observed from baseline period (1990–2003), while in C1, the yield is reduced around 5%. Yield doubling is attributed to the increase in rainfall, while yield decrease is related to the selection of a later planting date, which is indistinct to the SST-ensembles used for the calculation. Moreover, lower yields are related to years in which El Niño Southerm Oscilation (ENSO) are projected to occur at the end of century. The results are important as they provide a mitigation strategy for maize producers under rainfed model on the Azuero region, which is responsible for over 95% of the production of the country.

Assessment of weather-yield relations of starchy maize at different scales in Peru to support the NDC implementation

Climate change poses a substantial risk to agricultural production in Peru. Nationally Determined Contributions (NDCs) are currently developed and outline Peru's mitigation actions and adaptation plans to climate change in various sectors. To support the implementation of adaptation measures in the agricultural sector, information on weather-related risks for crop production and the effectiveness of adaptation options on the local scale are needed. We assess weather influences on starchy maize yields on different scales in Peru based on statistical crop models and a machine learning algorithm. The models explain 91% of yield variability (55% based on the cross-validation) on the regional scale. On the local scale, weather-related yield variation can be explained in some areas, but to a lower extent. Based on these models, we assess the effectiveness of adaptation measures which increase water availability to protect against negative impacts from dry weather conditions. The results show that a higher water availability of 77mm in the growing season would have regionally different effects, ranging from an increase of 20% to a decrease of 17% in maize yields. This large range underlines the importance of a local assessment of adaptation options. With this example, we illustrate how a statistical approach can support a risk-informed selection of adaptation measures at the local scale as suggested in Peru's NDC implementation plan.

The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)

Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: that growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts.

Weather dataset choice introduces uncertainty to estimates of crop yield responses to climate variability and change

Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983–2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.

Mid-20th century warming hole boosts US maize yields

The Corn Belt of the United States, one of the most agriculturally productive regions in the world, experienced a globally anomalous decrease in annual temperatures and a concurrent increase in precipitation during the mid- to late-20th century. Here, we quantify the impact of this ‘warming hole’ on maize yields by developing alternative, no warming hole, climate scenarios that are used to drive both statistical and process-based crop models. We show that the warming hole increased maize yields by 5%–10% per year, with lower temperatures responsible for 62% of the simulated yield increase and greater precipitation responsible for the rest. The observed cooling and wetting associated with the warming hole produced increased yields through two complementary mechanisms: slower crop development which resulted in prolonged time to maturity, and lower drought stress. Our results underscore the relative lack of climate change impacts on central US maize production to date, and the potential compounded challenge that a collapse of the warming hole and climate change would create for farmers across the Corn Belt.

Uncertainties in the Effects of Climate Change on Maize Yield Simulation in Jilin Province: A Case Study

Measuring the impacts of uncertainties identified from different global climate models (GCMs), representative concentration pathways (RCPs), and parameters of statistical crop models on the projected effects of climate change on crop yields can help to improve the availability of simulation results. The quantification and separation of different sources of uncertainty also help to improve understanding of impacts of uncertainties and provide a theoretical basis for their reduction. In this study, uncertainties of maize yield predictions are evaluated by using 30 sets of parameters from statistical crop models together with eight GCMs with reference to three emission scenarios for Jilin Province of northeastern China. Regression models using replicates based on bootstrap resampling reveal that yields are maximized when the optimum average growing season temperature is 20.1°C for 1990–2009. The results of multi-model ensemble simulations show a maize yield reduction of 11%, with 75% probability for 2040–69 relative to the baseline period of 1976–2005. We decompose the variance so as to understand the relative importance of different sources of uncertainty, such as GCMs, RCPs, and statistical model parameters. The greatest proportion of uncertainty (> 50%) is derived from GCMs, followed by RCPs with a proportion of 28% and statistical crop model parameters with a proportion of 20% of total ensemble uncertainty.

Climate impacts on long-term silage maize yield in Germany

In this study, we examine the impacts of climate change on variations in the long-term mean silage maize yield using a statistical crop model at the county level in Germany. The explanatory variables, which consider sub-seasonal effects, are soil moisture anomalies for June and August and precipitation and temperature for July. Climate projections from five regional climate models (RCMs) are used to simulate soil moisture with the mesoscale Hydrologic Model and force the statistical crop model. The results indicate an average yield reduction of −120 to −1050 (kilogram/hectare)/annum (kg ha−1 a−1) for the period 2021–2050 compared to the baseline period 1971–2000. The multi-model yield decreases between −370 and −3910 kg ha−1 a−1 until the end of the century (2070–2099). The maximum projected mean loss is less than 10% in magnitude of average yields in Germany in 1999–2015. The crop model shows a strong ability to project long-term mean yield changes but is not designed to capture inter-annual variations. Based on the RCM outcomes, July temperature and August soil moisture anomalies are the main factors for the projected yield anomalies. Furthermore, effects such as adaptation and CO2 fertilization are not included in our model. Accounting for these might lead to a slight overall increase in the future silage maize yield of Germany.

Toward building a transparent statistical model for improving crop yield prediction: Modeling rainfed corn in the U.S

Statistical crop models have been a major tool in identifying critical drivers of crop yield, forecasting short-term crop yield, and assessing long-term climate change impacts on agricultural productivity. However, few studies focus specifically on fundamental issues encountered in developing a high-performance statistical crop model for yield prediction. Such issues include: how to select predictors and fitting functions, how to effectively address the spatiotemporal scale issue, weather it is beneficial to include satellite data as explanatory variables, and how to reconcile different model evaluation procedures. In this study, we present our statistical modeling practices for predicting rainfed corn yield in the Midwest U.S. and address the aforementioned issues through comprehensive diagnostic analysis. Our results show that vapor pressure deficit and precipitation at a monthly scale, in spline form with customized knots, define the “Best Climate-only” model among alternative climate variables (e.g., air temperature) and fitting functions (e.g., linear or polynomial), with an out-of-sample (leave-one-year-out) median R2 of 0.79 and RMSE of 1.04 t/ha (16.6 bu/acre) from 2003 to 2016. Satellite variables, such as MODIS land surface temperature and Enhanced Vegetation Index (EVI), when used as predictors alone, reduce the model’s RMSE to 0.93 t/ha (14.8 bu/acre). Adding satellite variables (i.e., EVI in polynomial form) to the “Best Climate-only” model gives the “Best Climate + EVI” model, which has the highest prediction performance of this study, with a median R2 of 0.85 and RMSE of 0.90 t/ha (14.3 bu/acre). Such a model trained using all data (so-called “global model”) in most cases leads to better predictions than the state-specific trained models. However, the global model’s prediction performance exhibits considerable regional and interannual variations. The regional-varying performance is related to states’ spatiotemporal variability in yield, where states with larger spatial yield variability show higher R2, and states with smaller temporal yield variability show lower RMSE. Interannual variations in prediction performance are linked to yield variability and degree of wetness, with higher R2 in years with larger yield variability but increasingly larger RMSE toward wetter years and extreme dry years. These identified spatial and temporal variations of model’s performance, together with inconsistent evaluation practices undermine the comparability between statistical modeling studies. Alleviating such comparability issues requires more transparency and open data practices. The statistical model presented in this study provides a benchmark for further development and can be applied to future research related to yield prediction or assessment of climate change impact.

Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China

Agro-meteorological condition plays a fundamental role in crop production. For a specific region, the comprehensive effects of multiple meteorological factors are important indicators for the climatic suitability of the crops. To evaluate the synthetic effects, an integrated climatic assessment indicator (ICAI) are developed in Jiangsu Province, China. A newly produced meteorological assimilation driving datasets (CMADS V1.0) combined with observation data are used in establishing the indicator. The procedure to construct the indicator involves building statistical crop models by meteorological factors and determining the indicator values by classification. In modeling, two machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM) are compared and the classification model of RF is chosen to build ICAI due to its better performance in the independent test set. To determine a reasonable division in classification, distribution detection of climatic yield is carried out and Monte Carlo simulations are applied for the Kolmogorov–Smirnov (KS) test. The generated indicator includes three values: yield loss, normal and yield increment, with the spatial and temporal prediction accuracy from 67.86% to 100% in the test set for the Northern, Central and Southern Jiangsu. The ICAI are used to estimate the past climatic suitability of winter wheat and the future suitability under global warming conditions in Jiangsu Province. The results show that the climate in 1990s has more adverse effects on wheat production than the other two sub-periods in Northern and Southern Jiangsu. The adaptability of wheat production in Southern Jiangsu has improved greatly to the local environments during the past three decades. In addition, when annual temperature accelerates upwards, both possibilities of yield loss in Northern Jiangsu and yield increment in Southern Jiangsu will increase. Therefore, more concerns should be given to the North in future warming climate, while yield potential in the South may be further improved in this circumstance.

Automatic Responses of Crop Stocks and Policies Buffer Climate Change Effects on Crop Markets and Price Volatility

Climate change has the potential to affect crop prices and price volatility. However, the economic models used in prior assessments largely do not include known, automatic, stabilizing factors. Crop storage can stabilize prices and U.S. crop policy tends to provide support that moves opposite prices. We quantify effects of circa 2050 climate forcing on the inter-annual variability of U.S. Corn Belt corn and soybean yields using statistical crop models and climate scenarios from regional and global climate models. Climate change generally reduces mean yields and increases the inter-annual variability of yields in the Midwestern U.S. Using these yield impacts and an economic model with automatic market stabilizers, we find only modest increases in price volatility. Although individual producers and states are negatively affected by the yield reductions, the aggregate effect for all corn and soybean producer returns can be positive because of price increases. Moreover, agricultural policies based on price levels or revenue variation offset some of the impacts of market variation on farm income. Our results differ from other recent results and temper concerns that increasing climate instability necessarily translates to greater uncertainty about agricultural commodity uses, including as food and biofuels, in the near future.

Phenotypic Trait Identification Using a Multimodel Bayesian Method: A Case Study Using Photosynthesis in Brassica rapa Genotypes.

Agronomists have used statistical crop models to predict yield on a genotype-by-genotype basis. Mechanistic models, based on fundamental physiological processes common across plant taxa, will ultimately enable yield prediction applicable to diverse genotypes and crops. Here, genotypic information is combined with multiple mechanistically based models to characterize photosynthetic trait differentiation among genotypes of Brassica rapa. Infrared leaf gas exchange and chlorophyll fluorescence observations are analyzed using Bayesian methods. Three advantages of Bayesian approaches are employed: a hierarchical model structure, the testing of parameter estimates with posterior predictive checks and a multimodel complexity analysis. In all, eight models of photosynthesis are compared for fit to data and penalized for complexity using deviance information criteria (DIC) at the genotype scale. The multimodel evaluation improves the credibility of trait estimates using posterior distributions. Traits with important implications for yield in crops, including maximum rate of carboxylation (Vcmax) and maximum rate of electron transport (Jmax) show genotypic differentiation. B. rapa shows phenotypic diversity in causal traits with the potential for genetic enhancement of photosynthesis. This multimodel screening represents a statistically rigorous method for characterizing genotypic differences in traits with clear biophysical consequences to growth and productivity within large crop breeding populations with application across plant processes.

Quantifying the indirect impacts of climate on agriculture: an inter-method comparison

Climate change and increases in CO2 concentration affect the productivity of land, with implications for land use, land cover, and agricultural production. Much of the literature on the effect of climate on agriculture has focused on linking projections of changes in climate to process-based or statistical crop models. However, the changes in productivity have broader economic implications that cannot be quantified in crop models alone. How important are these socio-economic feedbacks to a comprehensive assessment of the impacts of climate change on agriculture? In this paper, we attempt to measure the importance of these interaction effects through an inter-method comparison between process models, statistical models, and integrated assessment model (IAMs). We find the impacts on crop yields vary widely between these three modeling approaches. Yield impacts generated by the IAMs are 20%–40% higher than the yield impacts generated by process-based or statistical crop models, with indirect climate effects adjusting yields by between −12% and +15% (e.g. input substitution and crop switching). The remaining effects are due to technological change.