Process-based Crop Models Research Articles

Heterogeneous impacts of excessive wetness on maize yields in China: Evidence from statistical yields and process-based crop models

Understanding the impacts of climate extremes on crop yields is critical for climate change adaptation and agricultural risk management. Excessive wetness is known to cause substantial damage to maize yields, but the heterogeneous impacts on yields based on growing stage, regional climate, and management practices are not well researched; additionally, it is uncertain how well state-of-the-art process-based crop models reproduce those responses. This study evaluated the impacts of excessive wetness on statistical and crop-model-simulated maize yields in China, with a special focus on the differences among growing stages, mean climatology, and irrigation, fertilization regimes and soil properties. Statistical maize yields exhibited negative responses to excessive wetness, with yield damage of 6%. Maize yield was most sensitive to excessive wetness in the flowering-to-maturity stage. Maize yields in wetter or colder counties, or places with a greater proportion of irrigation, nitrogen application rates and soil organic carbon, tended to be affected more severely. Multiple crop model ensemble simulations tended to over optimistically report positive maize yield responses to excessive wetness. Research on heterogeneous impacts of excessive wetness on maize yields could benefit agricultural risk management and improve process-based models.

Climate Change Affects the Utilization of Light and Heat Resources in Paddy Field on the Songnen Plain, China

Efficient utilization of light and heat resources is an important part of cleaner production. However, exploring the changes in light and heat resources utilization potential in paddy under future climate change is essential to make full use of the potential of rice varieties and ensure high-efficient, high-yield, and high-quality rice production, which has been seldom conducted. In our study, a process-based crop model (CERES-Rice) was calibrated and validated based on experiment data from the Songnen Plain of China, and then driven by multiple global climate models (GCMs) from the coupled model inter-comparison project (CMIP6) to predict rice growth period, yield, and light and heat resources utilization efficiency under future climate change conditions. The results indicated that the rice growth period would be shortened, especially in the high emission scenario (SSP585), while rice yield would increase slightly under the low and medium emission scenarios (SSP126 and SSP245), it decreased significantly under the high emission scenario (SSP585) in the long term (the 2080s) relative to the baseline of 2000–2019. The light and temperature resources utilization (ERT), light utilization efficiency (ER), and heat utilization efficiency (HUE) were selected as the light and heat resources utilization evaluation indexes. Compared with the base period, the mean ERT in the 2040s, 2060s, and 2080s were −6.46%, −6.01%, and −6.03% under SSP126, respectively. Under SSP245, the mean ERT were −7.89%, −8.41%, and −8.27%, respectively. Under SSP585, the mean ERT were −6.88%, −13.69%, and −28.84%, respectively. The ER would increase slightly, except for the 2080s under the high emission scenario. Moreover, the HUE would reduce as compared with the base period. The results of the analysis showed that the most significant meteorological factor affecting rice growth was temperature. Furthermore, under future climate conditions, optimizing the sowing date could make full use of climate resources to improve rice yield and light and heat resource utilization indexes, which is of great significance for agricultural cleaner production in the future.

Machine Learning Crop Yield Models Based on Meteorological Features and Comparison with a Process-Based Model

Abstract A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate. Significance Statement Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.

The potential of crop models in simulation of barley quality traits under changing climates: A review

Most of the experimental and modeling studies that evaluate the impacts of climate change and variability on barley have been focused on grain yield. However, little is known on the effects of combined change in temperature, CO 2 concentration, and extreme events on barley grain quality and how capable are the current process-based crop models capture the signal of climate change on quality traits. Here in this review, we initially explored the response of quality traits of barley to heat, drought, and CO 2 concentration from experiential studies. Next, we reviewed the state of the art of some of the current modeling approaches to capture grain quality. Lastly, we suggested possible opportunities to improve current models for tracking the detailed quality traits of barley. Heat and drought stress increase the protein concentration which has a negative effect on malting quality. The rise of CO 2 concentration significantly reduces the grain protein, again resulting in a decline of the malting and brewing quality since the nitrogen concentration of grains needs to be kept at a specific level. The current crop models that simulate barley grain quality are limited to simulation of grain nitrogen concentration, size, and number in response to climate extremes and CO 2 . Nevertheless, crop models fail to account for the complex interactions between the conflicting effects of rising temperatures and droughts as well as increasing CO 2 concentrations on grain protein. They have mainly adapted wheat models that cannot capture barley’s protein composition and whole grain malting quality. Implementation of experiments from gene to canopy scales which are explicitly designed to detect the interactions among environmental variables on detailed quality traits and couple the remote sensing plus data-driven approaches to crop models are possible opportunities to improve modeling of barley grain quality. The development of modeling routines can capture the detailed grain quality provide valuable tools for forming climate adaptive strategies. Equally important, they can guide breeding programs to develop climate-resilient but high-quality barley genotypes. • Barley quality response to climate change and available modeling are explored. • Malting quality declines due to elevated CO2 and heat/drought effects. • Current models do not capture environmental interactions on barley quality traits. • Targeted trials and better models needed to test adaptation strategies on grain quality.

Dynamic Prediction of Preharvest Strawberry Quality Traits as a Function of Environmental Factors

Fruit quality is of increasing importance for consumers but is a complex trait for growers, as it is affected by environment, genotype, and crop management interactions. Decision support tools, such as computer models that simulate crop growth and development can help optimize production but require further improvement to simulate quality aspects. The goal of this study was to apply the newly developed CROPGRO-Strawberry model in the Decision Support System for Agrotechnology Transfer (DSSAT) model framework and develop a module for the dynamic prediction of quality traits for strawberry. Experimental data from Florida with quality measurements from multiple harvests were correlated with indices based on preharvest weather conditions (temperature, radiation, rainfall) and simulated model parameters (evapotranspiration) during fruit development. Two quality relationships based on linear equations were identified and integrated into the model to simulate strawberry fruit soluble solids content (r2 = 0.89, d = 0.97) and titratable acidity (r2 = 0.55, d = 0.85) based on preharvest temperature. A strategic analysis with historical weather data for a subtropical growing region over a 10-year period showed the importance of seasonal climate variability for simulated strawberry yield and fruit quality across different harvest months. The improved CROPGRO-Strawberry model is the first process-based crop model to predict selected quality traits across multiple harvests throughout the season and can be extended to other crop models for which quality traits are important.

Framework to guide modeling single and multiple abiotic stresses in arable crops

With the occurrence of extreme events projected to increase under climate change, it is critical to assess the risk they pose to food security and identify suitable adaptation options. While mechanisms and impacts of climatic stressors (e.g. frost, drought, heat or flooding) have been studied individually, little is known their combined impacts on crops to be expected under actual production conditions. This lack of process knowledge is reflected in the few instances of crop models considering multiple stressors. Here we provide an overview of the representation of single stressors in process based crop models. From this basis, a framework to consider multiple stressors in current models is presented, defining four stressor combination types: 1. Single exposure; 2. No direct interaction; 3. Known interaction; and 4. Unknown interaction. An analytical framework from ecological sciences is then presented as an approach to consider when formulating algorithms for the 4th type of unknown interactions. In a final section, we discuss new data driven and model based exploration options to support understanding multiple stressor interactions in recognition of the challenges of experimentation around multiple stressors. We assert that process based modeling has a large and largely untapped potential to support scientific investigations of the underlying mechanisms driving crop response to multiple stressors.

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

Integration of machine learning into process-based modelling to improve simulation of complex crop responses

AbstractMachine 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.

Observational constraint of process crop models suggests higher risks for global maize yield under climate change

Projecting future changes in crop yield usually relies on process-based crop models, but the associated uncertainties (i.e. the range between models) are often high. In this study, a Machine Learning (i.e. Random Forest, RF) based observational constraining approach is proposed for reducing the uncertainties of future maize yield projections by seven process-based crop models. Based on the observationally constrained crop models, future changes in yield average and yield variability for the period 2080–2099 are investigated for the globe and top ten producing countries. Results show that the uncertainties of crop models for projecting future changes in yield average and yield variability can be largely reduced by 62% and 52% by the RF-based constraint, respectively, while only 4% and 16% of uncertainty reduction is achieved by traditional linear regression-based constraint. Compared to the raw simulations of future change in yield average (−5.13 ± 18.19%) and yield variability (−0.24 ± 1.47%), the constrained crop models project a much higher yield loss (−34.58 ± 6.93%) and an increase in yield variability (3.15 ± 0.71%) for the globe. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia. Our results suggest more agricultural risks under climate change than previously expected after observationally constraining crop models. The results obtained in this study point to the importance for observationally constraining process crop models for robust yield projections, and highlight the added value of using Machine Learning for reducing the associated uncertainties.

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.

Temporal Variation and Component Allocation Characteristics of Geometric and Physical Parameters of Maize Canopy for the Entire Growing Season

The accurate monitoring of crop parameters is important for crop yield prediction and canopy parameter inversion from remote sensing. Process-based and semi-empirical crop models are the main approaches to modeling the temporal changes in crop parameters. However, the former requires too many input parameters and the latter has the problem of poor portability. In this study, new semi-empirical geometric and physical parameters of the maize canopy model (GPMCM) crop model adapted to northeast China were proposed based on a time-series field datasets collected from 11 sites in the Nong’an and Changling Counties of Jilin Province, China, during DOY (day of year) 163 to DOY 278 in 2021. The allocation characteristics of and correlations between each maize canopy parameter were investigated for the whole growing season using the 22 algorithms of crop parameters, and the following conclusions were obtained. (1) The high correlation coefficient (R mean = 0.79) of LAI with other canopy parameters indicated that it was a good indicator for predicting other parameters. (2) Better performance was achieved by the regression method based on the two-stage simulation. The root-mean-squared error (RMSE) of geometric parameters including maize height, stem long radius, and short radius were 12.91 cm, 0.74 mm, and 0.73 mm, respectively, and the RMSE of the physical parameters including the FAGB, AGB, VWC, and RWC of the stems and leaves, ranged from 0.05 kg/m2 to 4.24 kg/m2 (2.0% to 12.9% for mean absolute percentage error (MAPE)). (3) The extension of the field-scale GPMCM to the 500 m MODIS-scale still provided a good accuracy (MAPE: 11% to 18.5%) and confirmed the feasibility of the large-scale application of the GPMCM. The proposed CPMCM can predict the temporal dynamics of maize geometric and physical parameters, and it is helpful to establish the forward and reverse models of remote sensing and improve the inversion accuracy of crop parameters.

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simulating crop growth. In this study, an innovative hybrid approach for estimating the leaf area index (LAI) of paddy rice using climate data was developed using ML and DNN regression methodologies. First, we investigated suitable ML regressors to explore the LAI estimation of rice based on the relationship between the LAI and three climate factors in two administrative rice-growing regions of South Korea. We found that of the 10 ML regressors explored, the random forest regressor was the most effective LAI estimator, and it even outperformed the DNN regressor, with model efficiencies of 0.88 in Cheorwon and 0.82 in Paju. In addition, we demonstrated that it would be feasible to simulate the LAI using climate factors based on the integration of the ML and DNN regressors in a process-based crop model. Therefore, we assume that the advancements presented in this study can enhance crop growth and productivity monitoring practices by incorporating a crop model with ML and DNN plans.

Exploring Trade-Offs Between Profit, Yield, and the Environmental Footprint of Potential Nitrogen Fertilizer Regulations in the US Midwest.

Multiple strategies are available that could reduce nitrogen (N) fertilizer use in agricultural systems, ranging from voluntary adoption of new N management practices by farmers to government regulations. However, these strategies have different economic and political costs, and their relative effectiveness in decreasing N leaching has not been evaluated at scale, particularly concerning potential trade-offs in crop yield and profitability. To inform policy efforts in the US Midwest, we quantified the effects of four policy scenarios designed to reduce fertilizer N inputs without sacrificing maize yields below 95%. A simulated dataset for economically optimum N rates and corresponding leaching losses was developed using a process-based crop model across 4,030 fields over 30 years. Policy scenarios were (1) higher N prices, (2) N leaching fee, (3) N balance fee, and (4) voluntary reduction of N use by farmers, each implemented under a range of sub-levels (low to high severity). Aggregated results show that all policies decreased N rates and N leaching, but this was associated with an exponential increase in economic costs. Achieving an N leaching reduction target of 20% has an estimated pollution control cost of 30–37 US$/ha, representing 147 million US$/year when scaled up to the state level, which is in the range of current government payments for existing conservation programs. Notably, such control of N losses would reduce the environmental impact of agriculture on water quality (externalities) by an estimated 524 million US$/year, representing an increase in society welfare of 377 million US$/year. Among the four policies, directly charging a fee on N leaching helped mitigate economic losses while improving the point source reduction effect (i.e., targeting fields that were leaching hotspots) and better internalization effect (i.e., targeting fields with higher environmental impact costs). This study provides actionable data to inform the development of cost-effective N fertilizer regulations by integrating changes in crop productivity and N losses in economic terms at the field level.

Assimilating leaf area index data into a sugarcane process-based crop model for improving yield estimation

The ability to estimate sugarcane yield is an important factor to improving the planning capacity of public and private sectors, and so food and energy security. One way of achieving this is by employing process-based crop models (PBM), which can be coupled to data assimilation (DA) algorithms to correct predictions along the crop season. While the application of PBMs often need careful parameterization or genotype-specific parameters, few studies focus on understanding the impacts of crop parametrization with different crop genotypes with DA. Moreover, dimensioning the number and timing of observations is key to effectively improve predictions with DA. This study assess the performance of a new sugarcane PBM (DSSAT/SAMUCA) coupled to three DA methods, and when the genotype-specific parameters are available or not. Data from 22 field experiments is utilized to compare the performance of using the ensemble Kalman filter (EnKF), ensemble smoother (ES) and weighted mean (WM) for assimilating leaf area index (LAI) to improve yields estimates. We also quantify the impact of using one genotype-specific calibration (cv. RB867515) on yield predictions of four non-calibrated genotypes (cv. NCo376, SP832847, R570, RB72454). Simulations of DA methods had better performance than employing the PBM without DA, so called open-loop (OP). The ES method resulted in the best performance (R² = 0.498 and RMSE = 20.268 Mg ha−1) followed by EnKF and WM. Utilizing a genotype-specific calibration showed substantially smaller RMSE for the three DA methods (EnKF = 16.76, ES = 16.70 and WM = 15.36 Mg ha−1) compared to non-calibrated (EnKF = 21.44–26.23, ES = 21.50–26.27 and WM = 23.38–28.37 Mg ha−1). Nevertheless, we also verified a higher improvement of model performance when applying EnKF and ES method to experiments where the cultivar does not match the genotype-specific calibration employed. While the WM had the opposite results, with the calibrated cultivar showing a higher improvement of model performance. As the number of LAI data assimilation increases, the DA methods tend to outperform the OP, but observations at late crop phenological stage of development showed a higher positive influence on SFY predictions.

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.

Designing wheat cultivar adaptation to future climate change across China by coupling biophysical modelling and machine learning

Wheat has been documented to be vulnerable to climate change in broad regions of the world including China. Adaptation to future climate change by breeding climate resilient cultivars is essential. However the precise information as to where, when, and what cultivar traits should be applied to adapt to climate change in the coming decades has not been available. In this study, we developed novel hybrid assessment models by incorporating a process-based crop model and machine learning algorithms based on a large number of cultivars field experiments data. The models were applied to assess the impact of climate change on wheat productivity and to identify the timescale of wheat cultivar adaptation in the major wheat cultivation regions across China. Wheat yield was projected to decrease on average by 6.3% (9.4%) in the 2050 s under RCP 4.5 (8.5), relative to the baseline period (1986–2005), without the CO2 effect. By contrast, it was projected to increase on average by 5.7% (8.1%) in the 2050 s with the CO2 effect, across the regions and cultivar-maturing traits in China. Solar radiation, precipitation, temperature, cultivar-maturing traits, and CO2 are critical factors affecting wheat productivity in the major wheat cultivation regions. About 44% (39%) and 68% (57%) of wheat planting grids would require cultivar renewal before 2050 (2040) under RCP 4.5 and 8.5 emission scenario, respectively, at a medium risk level without the CO2 effect. The cultivars with a long reproductive growth duration, high photosynthetic efficiency and large harvest index would be generally promising although there are specific traits desirable for certain regions. This study developed a novel framework to identify the precise information on where, when, and what cultivar traits should be applied for wheat to adapt to future climate change, helping the stakeholders to cope with climate change timely and precisely.

An Integrative Process-Based Model for Biomass and Yield Estimation of Hardneck Garlic (Allium sativum).

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future.

Performance of the SSM-iCrop model for predicting growth and nitrogen dynamics in winter wheat

Process-based crop models are essential tools for representing the fundamental interactions between the cropping environment (weather, soil, and management) and plant development, growth, resource use, and yield formation. Due to these capabilities, crop models are considered as an integral component of smart farming tools for evaluating and improving crop management at field, farm, and regional scales. However, prior to application of a crop model in geospatial decision support tools, its robustness should be established by comparing model predictions with observations from the target cropping environment. The objective of this study was to assess the performance of the Simple Simulation Model (SSM-iCrop) for predicting growth and nitrogen (N) dynamics of winter wheat (Triticum aestivum) cultivars in a temperate environment. Detailed plant and soil data were collected from three field experiments conducted with four widely-grown cultivars under four N application rates in Austria. Variation in N fertilisation and differences in soil properties and weather conditions in the three field experiments generated a wide range of observed crop total dry mass (585–2034 g m−2), N uptake (5–32 g N m−2), and grain yield (211–898 g m−2). The SSM-iCrop model required parameterisation of a relatively small number of plant input parameters. As these parameters could be directly calculated from the experimental data, except for two phenology-related coefficients, there was no need for calibrating the model. In initial simulations, SSM-iCrop was not able to predict the response of leaf area index (LAI) to decreasing N supply. Introducing an additional parameter defining the minimum stem N concentration from emergence to begin grain growth improved the model performance substantially. The simulated time-course of crop attributes through the growing season showed good overall correspondence with observed data. Across the three field experiments, the model performed well in simulating above-ground dry mass (CV=5.9, RMSE=115.6 g N m−2), grain yield (CV=1.9, RMSE=60.5 g N m−2), total crop N uptake (CV=4.5, RMSE=1.9 g N m−2), and grain N content (CV=1.1; RMSE=2.2 g N m−2). Overall, The results of this study confirmed the robustness of SSM-iCrop for predicting wheat development, growth, N dynamics, and yield in the target cropping environments. The relatively simple structure and high degree of transparency make the SSM-iCrop suitable for integration in smart farming tools for improving tactical decision making in crop production. This study also highlights the essential role of high-quality detailed experimental data for adequate parameterisation and evaluation of crop models..

Historical simulation of maize water footprints with a new global gridded crop model ACEA

Abstract. Crop water productivity is a key element of water and food security in the world and can be quantified by the water footprint (WF). Previous studies have looked at the spatially explicit distribution of crop WFs, but little is known about their temporal dynamics. Here, we present AquaCrop-Earth@lternatives (ACEA), a new process-based global gridded crop model that can simulate three consumptive WF components: green (WFg), blue from irrigation (WFbi), and blue from capillary rise (WFbc). The model is applied to analyse global maize production in 1986–2016 at 5×5 arcmin spatial resolution. Our results show that over the 2012–2016 period, the global average unit WF of maize is 728.0 m3 t−1 yr−1 (91.2 % WFg, 7.6 % WFbi, and 1.2 % WFbc), with values varying greatly around the world. Regions with high-input agriculture (e.g. Western Europe and Northern America) show small unit WFs and low interannual variability, while low-input regions show opposite outcomes (e.g. Middle and Eastern Africa). From 1986 to 2016, the global average unit WF reduced by a third, mainly due to the historical increase in maize yields. However, due to the rapid expansion of rainfed and irrigated areas, the global WF of maize production increased by half, peaking at 768.3×109 m3 yr−1 in 2016. As many regions still have a high potential in closing yield gaps, unit WFs are likely to reduce further. Simultaneously, humanity's rising demand for food and biofuels may further expand maize areas and hence increase WFs of production. Thus, it is important to address the sustainability and purpose of maize production, especially in those regions where it might endanger ecosystems and human livelihoods.

Adapting to climate change precisely through cultivars renewal for rice production across China: When, where, and what cultivars will be required?

Climate change is projected to have an important impact on crop productivity at broad regions of the world. Crop breeders across the globe have continuously been working on development of crop cultivars to adapt to climate change, the detailed information is necessary on when and where the adaptability of current cultivars will be broken and what cultivar traits will be required. Here, we developed a novel hybrid crop modelling approach that coupled a process-based crop model with machine learning and systematically assessed the impacts of climate change on rice productivity for 36 representative cultivars in China. We identified when, where, and what cultivars would be required for rice production to adapt to climate change precisely across China. We showed substantial differences in climate change impacts amongst cultivars. Current cultivars replacement could only alleviate but not offset the potential yield loss due to climate change in the future. Without adaptations and CO2 effect, nearly 67% of single rice and 46% of double rice cultivation areas would require cultivar renewal before 2050. Only two decades of leading time remain in the mid-lower reaches of the Yangtze River. The cultivars with a medium growth cycle, long grain-filling period, high photosynthetic capacity, and less spikelets should be breeding targets to adapt to climate change, although the ideotypic traits could be different for a specific environment and rice type.