Agricultural Production Systems Simulator Research Articles

Pearl millet phenology assessment: An integration of field, a review, and in silico approach

AbstractPearl millet [Pennisetum glaucum (L.) R.Br.] is an essential subsistence cereal for food security in dryland farming systems of the semiarid tropics (e.g., in sub‐Saharan Africa) and has improved tolerance to drought, heat, and salinity stress compared to other domesticated cereals. Assessing the variation on phenology is critical toward devising effective adaptative management strategies for crop adaptation to current and future climate change. In this context, pearl millet presents a vast genetic diversity, exhibiting sensitivity to temperature and photoperiod. Hence, this study aims to describe the genotypic variability in the phenological responses of pearl millet to temperature and photoperiod, particularly affecting leaf number with implications on the overall total time to flowering. The dataset encompassed 21 publications from seven countries, with experiments conducted from 1965 to 2023, including three field studies from the United States. Broad variability has been reported for phyllochron values ranging from 45 to 111°Cd leaf−1, with a mean value of 67°Cd leaf−1. Thermal time to panicle initiation ranged from 340 to 594°C, but no response to photoperiod duration was found due to the nature of dataset. Maximum number of leaves per shoot ranged from 11 to 25, showing response (1.55–2.15 leaf h−1) to photoperiod due to variations in thermal time to flowering (from 875 to 1346°Cd). Thermal time to flowering increased ca. 323°Cd h−1 under day durations longer than 13.3 h, below which basic vegetative phase duration was close to 1033°Cd. Based on the Agricultural Production Systems sIMulator simulations, different combinations of the above responses (in silico cultivars) generated a great range of times to flowering (44–120 days) for locations in Senegal, Brazil, India, and United States. The findings of this study can help breeders to explore the phenological genetic variability of pearl millet and provide inputs for crop growth models to evaluate future in silico scenarios.

Impact of fertilization depth on sunflower yield and nitrogen utilization: a perspective on soil nutrient and root system compatibility.

The depth of fertilizer application significantly influences soil nitrate concentration (SNC), sunflower root length density (RLD), sunflower nitrogen uptake (SNU), and yield. However, current studies cannot precisely capture subtle nutrient variations between soil layers and their complex relationships with root growth. They also struggle to assess the impact of different fertilizer application depths on sunflower root development and distribution as well as their response to the spatial and temporal distribution of nutrients. The Agricultural Production Systems sIMulator (APSIM) model was employed to explore the spatial and temporal patterns of nitrogen distribution in the soil at three controlled-release fertilizer (CRF) placement depths: 5, 15, and 25 cm. This study investigated the characteristics of the root system regarding nitrogen absorption and utilization and analyzed their correlation with sunflower yield formation. Furthermore, this study introduced the modified Jaccard index (considering the compatibility between soil nitrate and root length density) to analyze soil-root interactions, providing a deeper insight into how changes in CRF placement depth affect crop growth and nitrogen uptake efficiency. The results indicated that a fertilization depth of 15 cm improved the modified Jaccard index by 6.60% and 7.34% compared to 5 cm and 25 cm depths, respectively, maximizing sunflower yield (an increase of 9.44%) and nitrogen absorption rate (an increase of 5.40%). This depth promoted a greater Root Length Density (RLD), with an increases of 11.95% and 16.42% compared those at 5 cm and 25 cm, respectively, enhancing deeper root growth and improving nitrogen uptake. In contrast, shallow fertilization led to higher nitrate concentrations in the topsoil, whereas deeper fertilization increased the nitrate concentrations in the deeper soil layers. These results provide valuable insights for precision agriculture and sustainable soil management, highlighting the importance of optimizing root nitrogen absorption through tailored fertilization strategies to enhance crop production efficiency and minimize environmental impact.

Simulating within-field spatial and temporal corn yield response to nitrogen with APSIM model

ContextProcess-based crop growth models can explain soil and crop dynamics that influence the optimal N rate for crop production. Currently, there is a lack of understanding regarding the accuracy of process-based models for site-specific zones within fields, as well as the key factors that need to be considered when calibrating these models for zone-specific economic optimum N rate (EONR).ObjectiveWe calibrated the Agricultural Production Systems sIMulator (APSIM) model in contrasting zones within fields, quantified the model performance, and used the calibrated model to develop long-term corn yield response to N to assess the temporal variability between zones and sites to assist decision making.MethodsWe conducted four N rate experiments (2 fields × 2 zones within a field) over two years in southeast Nebraska. Experimental data were used to calibrate and test the APSIM model. APSIM simulated corn yield response to N for each zone and site was obtained by running numerous iterations of the calibrated model at different N rates. Observed and simulated corn yield response to N rate were analyzed with statistical models to estimate the EONR.Results and conclusionsThe APSIM model predicted corn yield over 11 historical years with a relative root mean square error (RRMSE) of 12% and yield at EONR in the N studies with RRMSE of 8.8%. The simulated EONR was lower than the observed EONR across sites, years, and zones with greater error than yield. The simulated yield increase with N fertilization was under-estimated in fine textured soils and over-estimated in medium textured soils. Long-term corn yield response to N showed that temporal variation in simulated EONR was greater than spatial variation. Long-term EONR and yield at EONR increased with increasing rainfall, while yield at zero N was greatest in normal years. Temporal variation was driven primarily by year-to-year variation in N loss (CV of 67% ± 9.5). Soil texture, hydrological properties, water table, and tile drainage were key variables for accurate site-specific model calibration. Improvements in simulating site-specific EONR may be realized by including in-situ or remotely sensed data for better estimation of N dynamics. We concluded that APSIM can provide valuable insights into systems dynamics in this region, but it can’t provide precise N-rate estimates. Our study contributes to understanding of the within-field variability using simulation modeling.

Sugarcane sustainability in a changing climate: a systematic review on pests, diseases, and adaptive strategies

The cultivation of sugarcane (Saccharum officinarum L.) in the face of climate change requires robust strategies for managing pests, diseases, and weeds. This systematic review exposes critical deficiencies in current practices and underscores the need for climate-adaptive strategies. Climate change differentially influences pest behaviour, disease progression, and weed growth across various regions, yet the lack of region-specific responses impairs effective management. The review emphasizes the necessity for localized approaches that consider specific climatic conditions and the development of predictive models to anticipate pest and disease outbreaks. These models include Decision Support Systems (DSS), Support Vector Machines (SVM), Susceptible-Exposed-Infectious-Recovered (SEIR) models, Geographic Information Systems (GIS), Species Distribution Models (SDMs), Agricultural Production Systems sIMulator (APSIM), and Integrated Pest Management (IPM). Crucial strategies encompass integrated pest and disease management, adaptive breeding, precision agriculture, and ongoing innovation. Precision agriculture technologies, such as remote sensing and drones, enable early detection and prompt interventions. By adopting these adaptive measures and addressing existing research gaps, the sugarcane industry can bolster its resilience and maintain productivity amidst evolving climatic conditions.Systematic review registrationhttps://www.bmj.com/content/372/bmj.n71.

Coupled weather and crop simulation modeling for smart irrigation planning: a review

ABSTRACT Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Construction of a High-Resolution Waterlogging Disaster Monitoring Framework Based on the APSIM Model: A Case Study of Jingzhou and Bengbu

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters.

Integrating rainfall index-based insurance with optimal crop management strategies can reduce financial risks for Australian dryland cotton farmers

Drought undermines the financial sustainability of farmers. While farmers have adopted various strategies to mitigate some drought impacts, they remain exposed to substantial drought risk. Insurance could be useful in managing climatic risks and for encouraging farmers to take sensible risks (e.g., changing their sowing date to increase yield), but it can be costly. Here, we tested whether the integration of a change in sowing date with rainfall index-based insurance could improve farmer profitability and income stability. We used the Agricultural Production Systems Simulator (APSIM)-Cotton model to simulate cotton lint yields for various sowing dates, taking into account different management strategies, across three dry-land cotton research farm sites – Dalby, Goondiwindi, and Theodore – from 1940 to 2022. We designed the index-based insurance payout when the average rainfall received during the growing season falls below a predefined level, such as the 5th, 10th, or 20th percentile of rainfall. Our study, which involved 3.9 million cotton lint simulations and 3,000 rainfall index-based insurance products, showed that combining a shift in sowing date with insurance can lead to an income improvement of up to 21.5% at some study sites. Additionally, in drought years, the income improvement for farmers who combined optimal sowing dates with rainfall index-based insurance was up to 48.0%. The framework developed in this study could aid in devising financial strategies to enhance farming resilience during climate extremes.

Exploring adaptation strategies for smallholder farmers in dryland farming systems and impact on pearl millet production under climate change in West Africa

Understanding and identifying appropriate adaptation optons for cropping systems and management practices at spatial and temporal scales is an important prerequisite for scaling. Pearl millet (Pennisetum glaucum (L) R. Br.) could be regarded as a risk-reducing measure crop under climate change when coupled with tactical agronomic management practices. In this study, we assess the impacts of adaptation strategies such as cultivar type, planting windows, and fertilizer strategies on pearl millet production under rainfed farming systems over Nigeria and Senegal using the Agricultural Production Systems Simulator (APSIM) model. The impact of climate change on millet yield was evaluated using a validated APSIM-millet module that utilized yield data collected through participatory research and extension approach (PREA) in contrasting environments. The climate model projections for the mid-century period (2040–2069) were compared against a baseline period of 1980–2009 for both locations. During the simulation, two millet varieties (improved local and dual-purpose) with two sowing regimes were considered comparing traditional farmers’ sowing window (dry sowing) and agronomic sowing window (planting based on the onset of the rainfall) at three different fertilizer levels [low (23 kg N ha−1), medium (40.5 kg N ha−1), and high (68.5 kg N ha−1) respectively]. The performance of the APSIM-millet module was found to be satisfactory as indicated by the low Root Means Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) values. The range for grain yield was between 17.7% and 25.8%, while for AGB it was between 18.6% and 21.4%. The results showed that farmers’ sowing window simulated slightly higher grain yield than the agronomic sowing window for improved local millet cultivar indicating yield increased by 8–12%. However, the projected changes in the mid-century (2040–2069) resulted in a decline in yield against baseline climate for both varieties and sowing windows, indicating the negative impact of climate change (CC) on yield productivity. The comparison between dual-purpose millet and improved local millet indicates that disseminating the improved millet variety and implementing early sowing could be an effective adaptation strategy in reducing risks and losses caused by climate change. Similarly, low magnitude impacts simulated on grain yield (< −8% in Nigeria compared to > −8% in Senegal) even though both locations are in the same agroecological zone.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model.

The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

Achieving stable and sustainable Mungbean yields in Australia via optimal sowing dates

ContextAs a rainfed, short-season crop, mungbean is a particularly attractive summer rotation option for Australian farmers, but the rate of mungbean adoption in Australia is low due to its high yield instability. A lack of research conducted on modern mungbean in Australia has hindered farmers’ ability to understand how to best manage the crop and close the yield gap. ObjectivesThe aim of this study was to provide recommendations for optimal sowing windows (balancing maximum yield potential with minimum risk—i.e. instability and crop failure). MethodsWe simulated 60 years of growing mungbean at 180 different sowing times, four different initial soil water levels and at seven sites across Queensland and New South Wales using the new Agricultural Production Systems sIMulator Next Generation (APSIM NextGen) Mungbean model. We then calculated the impact of sowing date on mungbean water use efficiency and on the soil water deficit for the subsequent crop in the rotation. ResultsHigher initial soil water levels decreased risk (instability and failure) at all sites. Arid sites with high evaporative demand had optimal sowing windows later (February) in the growing season. More temperate sites yielded better with higher levels of stability when sown between November and January. Sowing in September/October is not recommended in any site as it coincided with low yield stability and water use efficiency. Not all sites had a clear optimal sowing window. We conclude, therefore, that in some environments, sowing time can only marginally reduce risks in growing mungbean. ConclusionsWe identified optimal sowing window that can reduce risks of a failed crop by 20 % and increase stability without losing yield potential. The timing of the optimal sowing window for a given site appears to be influenced by avoiding periods with high evaporative demand during the reproductive phase. ImplicationsModelling has been critical for exploring the trade-offs between maximizing and stabilizing mungbean yields over a wide range of environments and growing seasons to determine the optimal sowing windows across the main growing environments in Australia.

Ranking genotype, environment, management effects on the optimum nitrogen rate for maize: A cropping system modeling analysis

AbstractRanking the contribution of genotype, environment, and management (G × E × M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G × E × M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G × M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G × E × M factors influenced the yield response to N fertilizer in different ways. Soil‐related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G × M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions.

Validating APSIM for the Northern Territory of Australia: An environment with challenging weather and soils

AbstractExtreme weather (high rainfall and temperatures) and challenging soils are sources of uncertainties in the use of current crop models that have been developed for more favorable environments. This may limit their applicability to guide and support decision making for the development of new agricultural regions in tropical environments. We evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) framework in representing yield and development of a range of crops across multiple locations in the Northern Territory of Australia, a tropical region with large potential for agricultural development. Observations of yield, biomass, and phenology for a range of crops from 28 experiments undertaken at three locations were compiled and used to develop simulations undertaken using APSIM version 7.10. Model performance varied with coefficients of determination and concordance correlation coefficients ranging from 0.36 to 0.98 and 0.37 to 0.93, respectively. Instances where model performance was less than ideal were associated with conditions presenting a limited number of observed values. Deviations by the model from yield observations were larger for situations with high‐yielding crops and low daily maximum temperatures during vegetative growth stages. Deviations in phenology were larger for conditions associated with water and N stress. APSIM was capable of representing the yield, biomass, and development of cereal and pulse crops and can be used with confidence to assist the expansion of agriculture in tropical environments such as the Northern Territory of Australia.

Seasonal climate forecast-an important tool in managing the risk of extreme weather events in Australia's wheat industry

This study aims to investigate the benefits of using the Australian Community Climate and Earth-System Simulator-Seasonal Version 2 (ACCESS-S2), a state-of-the-art seasonal climate forecast (SCF) system, to improve contingent decision-making in the Australian wheat industry. Six locations in eastern Australia, three cultivars with varying maturities, and 17 times of sowing (TOS) were considered. Seasonal hindcasts from ACCESS-S2, initialized on the 1st May for the period 1981–2018 were linked with the Agricultural Production System sIMulator (APSIM)-Wheat model (v7.10) to (1) assess the hindcast skill of ACCESS-S2 in predicting the occurrence of extreme weather events and in predicting above/below long-term median wheat yield; (2) identify optimal management strategies in mitigating the impact of extreme weather events; and (3) quantify the yield benefit of using ACCESS-S2 at the key decision-making point of sowing against a baseline management situation (i.e., without the use of SCF). Research results showed that (1) ACCESS-S2 has varying skills in predicting extreme climate indices and wheat yield across the six locations at a seasonal lead time; (2) 68 % of cases had a yield gain using SCF information; (3) across all cases, there was an average yield gain of 281 kg/ha representing an increase of 13 %; (4) benefits of using SCF were seen across predicted wet years, neutral years and dry years accounting for 69 %, 65 % and 72 % respectively, and (5) the identified optimal management options varied from location to location with earlier sowing associated with either suntop or gregory or both being the optimal strategies at most of the locations considered. Overall, there is a demonstrated benefit in utilizing ACCESS-S2 forecasts in the Australian wheat industry to improve farm management decision-making. The benefit can occur in any climate state but with dry years being more likely and significant (with an average yield increase of 97 % across the locations).

Integrating Crop Modeling and Machine Learning for the Improved Prediction of Dryland Wheat Yield

One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.

Sustainable management strategies for balancing crop yield, water use efficiency and greenhouse gas emissions

CONTEXTTo ensure food security in the face of climate change, it is crucial to use effective agricultural management practices that produce the triangular win-win for water, crops, and environments. OBJECTIVEThis study aimed to explore the sustainable management measures for balancing crop yield, water use efficiency (WUE) and greenhouse gas (GHG) emissions in China during 1981–2020. METHODSThe study selected the Beijing-Tianjin-Hebei (BTH) region of China as the research area, where there were sharp contradictions among water shortage, food security and polluted environment. Four irrigation treatments and four fertilization treatments were set referring to the local cultivated experiences. The daily dynamic changes in crop yield, WUE and GHG emissions were simulated by optimized Agricultural Production Systems sIMulator (APSIM) model. RESULTS AND CONCLUSIONSThe irrigations had positive impacts on crop yield and WUE in BTH region. The irrigation amount's rise caused a gradual augmentation in the total yield of winter wheat and summer maize from 1981 to 2020.The highest total yield was achieved under W4 irrigation treatment (320 mm water for winter wheat and 60 mm water for summer maize), with an annual mean of 24,579.2 kg·ha−1. The annual average WUE under the W3 irrigation treatment (240 mm water for winter wheat) was the highest, with an average of 13.9 kg·ha−1·mm−1. However, extensive fertilization exacerbated soil GHG emissions. The soil GHG emissions were found to be significant increase as the input amount of fertilization. The FI fertilization measure (no fertilization) was assessed to have the least impact on the environment, with an average GHG emissions of 0.4 Mg·CO2-eq·ha−1. The optimal goals of high yield, high WUE and low GHG emissions were achieved by choosing effective combination practices of water and nitrogen management. Overall, the W3 irrigation and F3 fertilization (180 kg/ha fertilizer for winter wheat and 120 kg/ha fertilizer for summer maize.) treatments had the maximum water-fertilizer production indexes in the BTH region. SIGNIFICANCEThis study highlights the importance of agricultural management measures for guaranteeing food security and environmental sustainability at the same time under climate change.

Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California

Context Computer-based crop simulation models are important tools for agricultural research and management. APSIM (Agricultural Production Systems sIMulator) is commonly used around the world but has not been widely validated in North America. Aims The objective of this work was to evaluate the reliability of APSIM for simulating wheat production in California, with the aim of providing guidance for future field research aimed at model calibration and validation. Methods Environmental and management data from state-wide wheat variety trials of common wheat (Triticum aestivum L.) were used to parameterise the APSIM-Wheat module (ver. 7.10 r4220). Simulated yield and protein data were compared with observed field trial results to test the reliability of APSIM simulations. Key results The most reliable simulation of grain yield had a root-mean-square error of 1040 kg/ha and normalised root-mean-square error of 16% relative to actual field data. Preliminary calibration of the model for Californian wheat varieties did not improve simulation accuracy or precision. Conclusions The accuracy or precision of the simulations was comparable to that of other tests of the APSIM-Wheat module in environments where it has not been previously calibrated but was considered too low to be reliable. The lack of reliability was due to the poor representation of local Californian wheat genotypes by existing APSIM cultivars, as well as possible lack of precision and accuracy of field data. Implications APSIM could be a valuable tool for wheat research and management in California; however, further research is needed to generate suitable field data for model calibration and validation.

Modeling long-term nitrogen utilization under alfalfa–corn rotation in Northeast China

ContextNortheast China is the main commodity grain base in China owing to its intensive corn (Zea mays L.) planting and heavy nitrogen (N) application. Alfalfa (Medicago sativa L.) and corn rotation have the potential to decrease N application and enable efficient N utilization, but its long-term impacts remain unclear. ObjectiveThis study utilized Agricultural Production Systems sIMulator (APSIM) to evaluate the N process of long-term alfalfa–corn rotation, including the soil N status and N legacy benefits of subsequent corn crops, and to optimize alfalfa–corn rotation. MethodsAPSIM was calibrated and validated utilizing datasets of crop N uptake (Nupt), soil inorganic N, and soil total nitrogen (TN) under continuous corn, continuous alfalfa, and rotations of three, four, and five years of alfalfa followed by two years of corn, defined as CC, CA, and 3A2C, 4A2C, and 5A2C treatments, respectively, which were collected from a field experiment in Quanyangou–Lishu, Jilin Province, China during 2014–2022. The validated APSIM was then utilized to simulate long-term (1981–2020) effects on N utilization under CC, 3A2C, 4A2C, and 5A2C treatments at Quanyangou–Lishu, Fujia–Lishu, Chaoyang, and Hailun in Northeast China. ResultsAPSIM effectively simulated the Nupt, inorganic N, and TN, with normalized root mean square errors of 5%–19%, 20%–30%, and 2.0%–4.2%, respectively, between the observed and simulated values. Compared with the CC treatment, alfalfa–corn rotation treatments reduced the N fertilizer application by 143 kg N ha−1 and significantly improved the net N mineralization and Nupt by 41 and 107 kg N ha−1, respectively, achieving increases of 50% and 517% in N use efficiency and N partial factor productivity based on corn equivalent yield, respectively. Alfalfa–corn rotation also consumed 94% of 200–500 cm inorganic N through alfalfa roots absorbing N after 9–14 years, which decreased the risk of soil inorganic N accumulation and N leaching. After 40 years, SON values of alfalfa–corn rotation treatments were 8% and 4% higher in the 0–20 and 20–60 cm soil layers and 1.6% lower in the 60–200 cm soil layer than those of the CC treatment at Quanyangou–Lishu, Fujia–Lishu and Hailun. The SON values of alfalfa–corn rotation treatments were 2.9% lower in three soil layers at 0–200 cm deep relative to those of the CC treatment at Chaoyang, with low precipitation of only 478 mm. Alfalfa–corn rotation also improved the first-year corn yield following alfalfa by 2.3%–8.8% at Quanyangou–Lishu and Fujia–Lishu. ConclusionsThe 4A2C treatment at Quanyangou–Lishu, Fujia–Lishu, and Hailun and the 3A2C treatment at Chaoyang are recommended in Northeast China to increase N use efficiency and N partial factor productivity by decreasing N fertilizer application, increasing SON, and absorbing inorganic N from the 200–500 cm soil layer during the alfalfa growing period and increasing mineral N during the corn growing period. ImplicationsOptimal alfalfa–corn rotations show great potential to reduce soil inorganic N residue and increase N use efficiency and SON in Northeast China.

A framework for assessing the impacts of land-use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale.

Irrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land-use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch-generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed. Both LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 and 30.7 mm per growing season under 1.5 and 2.0 °C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5 and 2.0 °C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5 °C warming, and 13.1%, 28.7%, and 58.2% under 2.0 °C warming, respectively. We suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation. © 2023 Society of Chemical Industry.

Unveiling the resilience of smallholder farmers in Senegal amidst extreme climate conditions

AbstractIn Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.