Agricultural Production Systems Simulator Research Articles

Effects of Changing Climate Extremes on Maize Grain Yield in Northeast China

The increased frequency of climate extremes associated with ongoing climate change has the potential for significant impacts on maize grain yield in maize-producing regions worldwide. In this study, we used a modeling approach to investigate the effects of different types of climatic extremes on maize grain yield in Northeast China. We combined annual mean air temperature and accumulated precipitation data and divided the climate years into 9 categories, using the 10th and 90th percentiles as extreme thresholds. The climate data were represented by 1 normal climate type, 4 individual climate extremes, and 4 combined climate extremes. Changes in the frequencies of climate extremes and their impacts on maize grain yields were analyzed for two 30-year periods (period I: 1960–1989; period II: 1990–2019) using the Agricultural Production Systems sIMulator (APSIM-Maize). Compared with period I, the occurrences of the normal climate and all 3 cold-related climate extremes (i.e., cold-normal, cold-humid, and cold-dry) decreased during period II, while occurrences of the other climate extremes increased. Moreover, according to the APSIM-Maize model, the estimated yield in the years with a normal climate decreased by 4.01%; the 3 cold-related climate extremes increased yields by 26.56%, 12.10%, and 6.68%, respectively; the 2 warm-related climate extremes (warm-normal and warm-dry) and normal-humid years decreased estimated yields by 6.39%, 24.12%, and 5.10%, respectively. The increase in the occurrence of warm-humid years increased the estimated yield by 11.69%. This work confirms the adverse effects of warmer temperatures in the absence of excess rainfall on grain yield and highlights the importance of extremely dry or wet conditions under normal temperature conditions.

Earlier sowing combined with nitrogen fertilization to adapt to climate change effects on yield of winter wheat in arid environments: Results from a field and modeling study

Climate change effects on crop production are of high concern in arid regions that are suffering from increased drought. We parameterized the APSIM-Wheat (The Agricultural Production Systems sIMulator) model using data of two field experiments conducted in 2017 at two locations (Karaj and Khomein) in Iran differing in temperature and precipitation. The experiment was a split plot with four replications. Main plots measured 17 m by 5 m with four irrigation regimes of full irrigation (I1), deficit irrigation from grain filling (I2,), from beginning of flowering (I2), and during the whole growth period (I4). Sub-plots measured 3 m by 5 m with nitrogen (N) levels of 200 (N1), 100 (N2) and 50 (N3) kg/ha of urea (46 % N). N1 resulted in highest wheat biomass, grain yield, harvesting index and leaf area index (LAI), with the differences between the N levels decreasing with increasing water deficit period. The model was parameterized for both localities with data of treatment I1 +N1, then evaluated over 11 combinations of irrigation and nitrogen rates. Root mean square of errors (RMSE) were 0.3 and 0.15 t/ha for wheat biomass and grain yield, and 3.54 days for wheat phenology and 0.28 for LAI simulations. After model calibration, we ran the model with 20 general circulation models (GCMs) under two representative concentration pathways (RCP 4.5 and RCP 8.5), and crop production was projected for three future time periods. For 2010–2039, models simulate a marginal increase in wheat yield, however, with 2040–2069 and 2070–2099, most models simulate a decreased growing season length and grain yield. Nitrogen application was found to decrease the drought impact. Additionally, model simulation suggests that earlier sowing time and excessive irrigation are of benefit in adapting to climate change impact. Thus, in arid environments where additional irrigation is not an option, increased nitrogen application in combination with an earlier sowing time could be an effective adaptation strategy for future wheat production.

Agro-climatic sensitivity analysis for sustainable crop diversification; the case of Proso millet (Panicum miliaceum L.).

Current agricultural production depends on very limited species grown as monocultures that are highly vulnerable to climate change, presenting a threat to the sustainability of agri-food systems. However, many hundreds of neglected crop species have the potential to cater to the challenges of climate change by means of resilience to adverse climate conditions. Proso millet (Panicum miliaceum L.), one of the underutilised minor millets grown as a rainfed subsistence crop, was selected in this study as an exemplary climate-resilient crop. Using a previously calibrated version of the Agricultural Production Systems Simulator (APSIM), the sensitivity of the crop to changes in temperature and precipitation was studied using the protocol of the Coordinated Climate Crop Modelling Project (C3MP). The future (2040-2069) production was simulated using bias-corrected climate data from 20 general circulation models of the Coupled Model Intercomparison Project (CMIP5) under RCP4.5 and 8.5 scenarios. According to the C3MP analysis, we found a 1°C increment of temperature decreased the yield by 5-10% at zero rainfall change. However, Proso millet yields increased by 5% within a restricted climate change space of up to 2°C of warming with increased rainfall. Simulated future climate yields were lower than the simulated yields under the baseline climate of the 1980-2009 period (mean 1707 kg ha-1) under both RCP4.5 (-7.3%) and RCP8.5 (-16.6%) though these changes were not significantly (p > 0.05) different from the baseline yields. Proso millet is currently cultivated in limited areas of Sri Lanka, but our yield mapping shows the potential for expansion of the crop to new areas under both current and future climates. The results of the study, indicating minor impacts from projected climate change, reveal that Proso millet is an excellent candidate for low-input farming systems under changing climate. More generally, through this study, a framework that can be used to assess the climate sensitivity of underutilized crops was also developed.

Responses of wheat kernel weight to diverse allelic combinations under projected climate change conditions.

In wheat, kernel weight (KW) is a key determinant of grain yield (GY). However, it is often overlooked when improving wheat productivity under climate warming. Moreover, little is known about the complex effects of genetic and climatic factors on KW. Here, we explored the responses of wheat KW to diverse allelic combinations under projected climate warming conditions. To focus on KW, we selected a subset of 81 out of 209 wheat varieties with similar GY, biomass, and kernel number (KN) and focused on their thousand-kernel weight (TKW). We genotyped them at eight kompetitive allele-specific polymerase chain reaction markers closely associated with TKW. Subsequently, we calibrated and evaluated the process-based model known as Agricultural Production Systems Simulator (APSIM-Wheat) based on a unique dataset including phenotyping, genotyping, climate, soil physicochemistry, and on-farm management information. We then used the calibrated APSIM-Wheat model to estimate TKW under eight allelic combinations (81 wheat varieties), seven sowing dates, and the shared socioeconomic pathways (SSPs) designated SSP2-4.5 and SSP5-8.5, driven by climate projections from five General Circulation Models (GCMs) BCC-CSM2-MR, CanESM5, EC-Earth3-Veg, MIROC-ES2L, and UKESM1-0-LL. The APSIM-Wheat model reliably simulated wheat TKW with a root mean square error (RMSE) of < 3.076g TK-1 and R2 of > 0.575 (P < 0.001). The analysis of variance based on the simulation output showed that allelic combination, climate scenario, and sowing date extremely significantly affected TKW (P < 0.001). The impact of the interaction allelic combination × climate scenario on TKW was also significant (P < 0.05). Meanwhile, the variety parameters and their relative importance in the APSIM-Wheat model accorded with the expression of the allelic combinations. Under the projected climate scenarios, the favorable allelic combinations (TaCKX-D1b + Hap-7A-1 + Hap-T + Hap-6A-G + Hap-6B-1 + H1g + A1b for SSP2-4.5 and SSP5-8.5) mitigated the negative effects of climate change on TKW. The present study demonstrated that optimizing favorable allelic combinations can help achieve high wheat TKW. The findings of this study clarify the responses of wheat KW to diverse allelic combinations under projected climate change conditions. Additionally, the present study provides theoretical and practical reference for marker-assisted selection of high TKW in wheat breeding.

Performance of APSIM to Simulate the Dynamics of Winter Wheat Growth, Phenology, and Nitrogen Uptake from Early Growth Stages to Maturity in Northern Europe

Performance of the APSIM (Agricultural Production Systems sIMulator) wheat model was assessed to simulate winter wheat phenology, biomass, grain yield, and nitrogen (N) uptake for its potential to optimize fertilizer applications for optimal crop growth and minimal environmental degradation. The calibration and evaluation dataset had 144 and 72 different field growing conditions (location (~7) × year (~5) × sowing date (2) × N treatment (7-13)), respectively, and included seven cultivars. APSIM simulated phenological stages satisfactorily with both model calibration and evaluation data sets with r2 of 0.97 and RMSE of 3.98-4.15 BBCH (BASF, Bayer, Ciba-Geigy, and Hoechst) scale. Simulations for biomass accumulation and N uptake during early growth stages (BBCH 28-49) were also reasonable with r2 of 0.65 and RMSE of 1510 kg ha-1, and r2 of 0.64-0.66 and RMSE of 28-39 kg N ha-1, respectively, with a higher accuracy during booting (BBCH 45-47). Overestimation of N uptake during stem elongation (BBCH 32-39) was attributed to (1) high inter-annual variability in simulations, and (2) high sensitivity of parameters regulating N uptake from soil. Calibration accuracy of grain yield and grain N was higher than that of biomass and N uptake at the early growth stages. APSIM wheat model showed high potential for optimizing fertilizer management in winter wheat cultivation in Northern Europe.

Comparing water related indicators and comprehensively evaluating cropping systems and irrigation strategies in the North China Plain for sustainable production

The fully irrigated winter wheat–summer maize double cropping system (DWM) has caused groundwater overexploitation and aggravated water scarcity in the North China Plain (NCP). Therefore, it is necessary to select alternative cropping systems with effective irrigation strategies to maintain the sustainable use of groundwater and maintain crop productivity. However, the evaluation indicators, especially the water-related indicators of cropping systems and irrigation strategies in current studies are not consistent and it is difficult to obtain a comprehensive evaluation result. Hence, to overcome these challenges, this study reviewed and compared groundwater indicators, water use indicators, water production performance indicators and output indicators, and summarized twelve evaluation combinations. Four evaluation combinations with grain yield as output indicator were selected to comprehensively evaluate the cropping systems and irrigation strategies developed by Agricultural Production Systems sIMulator (APSIM) at a typical site in the NCP in the past 52 years. The indicator weights were determined by analytic hierarchy process, entropy weight method, random forest algorithm and principal component analysis. Evaluation results showed that when the main goal was to guarantee food safety or improve the beneficial water use, the DWM was selected as the best choice and the WMM (winter wheat–summer maize → spring maize system, three crops in two years) was the alternative cropping system. Under this condition, water saving irrigation should be vigorously developed to reduce groundwater exploitation. When the four indicator types had the same weight, the DWM under rainfed was the best choice to improve evapotranspiration use efficiency and precipitation use efficiency; the monocropping spring maize system under normal irrigation strategy was the best choice to reduce water footprint. The WMM under rainfed was the alternative choice to improve evapotranspiration use efficiency, improve precipitation use efficiency and reduce water footprint. The evaluation methods proposed in this study could be used as reference in the comparison of cropping systems and irrigation strategies in other water-related situations.

Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India.

Hybrid rice requires adequate nitrogen (N) management in order to achieve good yields from its vegetative and reproductive development. With this backdrop, a field experiment was conducted at Regional Research Station (Coastal Saline Zone), Bidhan Chandra Krishi Viswavidyalaya, Kakdwip, West Bengal (India) to record growth and yield performance of hybrid rice (cv. PAN 2423) under varied N-fertilizer doses. A modelling approach was adopted for the first time in hybrid rice production system under coastal ecosystem of West Bengal (India). In the present study, the Agricultural Production Systems Simulator (APSIM) model was calibrated and validated for simulating a hybrid rice production system with different N rates. The APSIM based crop simulation model was found to capture the physiological changes of hybrid rice under varied N rates effectively. While studying the relationship between simulated and observed yield data, we observed that the equations developed by APSIM were significant with higher R2 values (≥0.812). However, APSIM caused an over-estimation for calibrate data but it was rectified for validated data. The RMSE of models for all the cases was less than respective SD values and the normalized RMSE values were ≤20%. Hence, it was proved to be a good rationalized modelling and the performance of APSIM was robust. On the contrary, APSIM underestimated the calibrated amount of N (kg ha-1) in storage organ of hybrid rice, which was later rectified in case of validated data. A strong correlation existed between the observed and APSIM-simulated amounts of N in storage organ of hybrid rice (R2 = 0.94** and 0.96** for the calibration and validation data sets, respectively), which indicates the robustness of the APSIM simulation study. Scenario analysis also suggests that the optimal N rate will increase from 160 to 200 kg N ha-1 for the greatest hybrid rice production in coming years under elevated CO2 levels in the atmosphere. The APSIM-Oryza crop model had successfully predicted the variation in aboveground biomass and grain yield of hybrid rice under different climatic conditions.

Early–maturing cultivar of winter wheat is more adaptable to elevated [CO2] and rising temperature in the eastern Loess Plateau

Understanding the impacts of rising temperature and elevated [CO2] on different crop cultivars is critical to devising agronomic strategies and cultivar selection in the context of climate change adaptation. The growth period, biomass, and yield of two wheat cultivars (early– vs. late–maturing) were simulated by the Agricultural Production Systems sIMulator (APSIM) validated with pot experiments under the scenarios of elevated [CO2] and 2 °C warming based on the baseline climate for 1961–2016 at Taigu in North China. We also investigated the effects of extreme high temperature on the yield and yield components of these wheat cultivars. We found that APSIM well simulated the changes in the growth period, leaf area index (LAI), biomass, and yield induced by 2 °C warming and elevated [CO2]. Long–term simulations showed that 2 °C warming decreased the growing period between sowing and jointing for both cultivars. 2 °C warming and elevated [CO2] affected the wheat yield mainly by altering the grain numbers. Extreme high temperature decreased the grain numbers and yield of the late–maturing cultivar but not the early–maturing one. The findings suggest that early–maturing cultivar would be better adapted to elevated [CO2] and rising temperature than the late–maturing one in the eastern Loess Plateau.

Calibration of a crop growth model in APSIM for 15 publicly available corn hybrids in North America

AbstractApplication of crop growth models (CGMs) in plant breeding is limited by the large number of candidate cultivars that breeders work with and the large number of CGM parameters that affect cultivar performance. The objectives of this study were to (1) calibrate 15 publicly available maize hybrids in Agricultural Production Systems sIMulator and quantify prediction accuracy in modeling physiological trait differences (yield, biomass, phenology, etc.) among genotypes; (2) better understand minimum phenotypic data requirements for CGM cultivar calibration to inform breeding efforts; and (3) quantify simulated genotype by environment interactions (G × E) across years for five traits. We calibrated hybrids with two years of multi‐trait, temporal field measurements. The R2 of simulated versus observed phenotypes was 0.89 for grain yield and over 0.80 for half of all other simulated traits. Phenology parameters accounted for nearly half of the variability in grain yield. Average (across traits) normalized root mean square error was reduced from 35% to 30% with calibration based on phenological measurements and was reduced to 20% with inclusion of physiological and nitrogen‐related measurements such as radiation use efficiency and grain nitrogen. Long‐term simulations demonstrated distinct G × E among the hybrids which accounted for 2%–29% of the total genetic variation across traits. Parameter values derived in this work will provide insight regarding important physiological traits for further phenotyping, selection, and understanding of G × E. These calibrations are for publicly available hybrids, which are currently lacking.

Impacts of Climate Change on Rice Grain: A Literature Review on What Is Happening, and How Should We Proceed?

More than half of the people on Earth get their calories, proteins, and minerals from rice grains. Staple increases in the quantity and quality of rice grains are key to ending hunger and malnutrition. Rice production, however, is vulnerable to climate change, and the climate on Earth is becoming more fluctuating with the atmospheric change induced by human activities. As a result, the impacts of climate change on rice grain (ICCRG) have sparked widespread concern. In order to reveal the development and the trend in the study on the ICCRG, a bibliometric analysis was conducted. The results showed that both the model simulations and the field experiment-based observations, as reflected by APSIM (the Agricultural Production Systems sIMulator) and free-air carbon dioxide (CO2) enrichment, are of concern to researchers worldwide, especially in China, India, the United States, and Japan. Different types of warming include short-term, nighttime, soil and water, and canopy, and their interactions with other climate factors, such as CO2, or agronomic factors, such as nitrogen level, are also of concern to researchers. Spatiotemporal variations in changing weather and regional adaptations from developed and developing countries are challenging the evaluation of ICCRG from an economic perspective. In order to improve the efficacy of breeding adaptable cultivars and developing agronomic management, interdisciplinary studies integrating molecular biology, plant physiology, agronomy, food chemistry, ecology, and socioeconomics are needed.

Integrating Field Data and a Modeling Approach to Inform Optimum Planting Date × Maturity Group for Soybeans under Current and Future Weather Conditions in Kansas

Optimizing planting date by maturity group (PD × MG) is critical to increase productivity and reduce production risks. Understanding the effect of management, not only under current, but also future weather conditions, is even more relevant for developing effective mitigation strategies. This paper provides an analysis of the optimum combinations of soybean PD × MG management in the central-eastern region of Kansas (United States) for both current and future weather conditions. Three geographical clusters illustrating the main environmental and management characteristics were defined within the central-eastern region of Kansas. The Agricultural Production Systems Simulator platform was employed to explore PD × MG combinations (PD from mid-April to mid-July; MG from III to VI) comparing current (2011–2021) and future (2042–2052) weather conditions. Overall, early planting dates produce greater yields, but reduce their stability over time (with a 15% increase in yield variation relative to late planting) across the clusters. Late planting dates resulted in a reduction close to 27% for soybean yields relative to those obtained by planting at early dates under current weather conditions. Furthermore, longer maturity groups (IV, V, and VI) resulted in a reduced yield penalty when planting time was delayed under the current weather conditions. However, this combination did not always represent the strategy that maximized yields.

Calibration and Validation of APSIM Millet Model for Proso millet (&lt;em&gt;Panicum miliaceum&lt;/em&gt; L.) Accessions as a Basis for Crop Diversification

Purpose: Proso millet (Panicum miliaceum L.) is an underutilised minor millet grown as a rain-fed crop in subsistence farming systems in tropical African and Asian countries including Sri Lanka. It is identified as a climate-resilient crop that exhibits a huge potential to diversify conventional farming systems, however few modelling studies have assessed this crop, especially in the tropics. The objective of this study is to calibrate and validate Agricultural Production Systems Simulator (APSIM) model for Proso millet. Research Method: The millet model of APSIM that was developed based on Pearl millet was calibrated for five farmer-selected Proso millet accessions using the data gathered from two field experiments. Yield data collected from 35 farmers’ fields under different crop management practices were used to validate the model. Findings: The observed phenology, leaf area index, above ground biomass and grain yield were not significantly different (p &gt; 0.05) from the simulated values of the calibrated model. The observed yields (1187±336 kg ha–1) in the farmers’ fields were not significantly (p &gt; 0.05) different from the simulated yields (1208±255 kg ha–1), suggesting a good calibration of the model. Research Limitations: Lack of field data under different abiotic stress conditions is a limitation for further validation of the model. Originality/ Value: APSIM millet model has not been tested for Proso millet previously. Derived genetic coefficients were successfully used to simulate Proso millet production.

Regional integrated assessment of climate change impact on cotton production in a semi-arid environment

Climate change has a negative impact on the productivity of agricultural crops at local, regional and global levels. Foodstuff security and sustainable livelihood of cotton farmers in the core cotton growing region in Punjab, Pakistan is under threat because of decreased yield due to climate change. The quantification of the integrated impact assessment of climate change for developing adaptation approaches for cotton is vital for improving productivity at a regional level and improving food security at a national level. Two crop models were evaluated with on-farm survey data of 165 farms employing stratified random sampling techniques. Representative agricultural pathways (RAPs) were developed for characterizing future cotton production. Global climate models (GCMs) depicted a rise of 3.6 and 4.3°C for maximum and minimum temperature, respectively, along with a decreased in rainfall of 600 mm under Representative Concentration Pathway (RCP) 8.5. The expected temperature rise for the hot-dry climate would cause a reduction in productivity of 35.3 and 39.2% by mid-century for RCP4.5 and RCP8.5, respectively, according to the Decision Support System for Agrotechnology Transfer (DSSAT) model, while the Agricultural Production System Simulator (APSIM) showed a reduction of 51.1 and 59.6%, respectively. Increases under the current adaptation of a 15% increase in each of nitrogen and planting density ranged from 1.1 to 6.3% for DSSAT and 2.6 to 8.2% for APSIM. Climate-adapted cotton productivity was projected to rise from 18.7 to 35.9% for DSSAT and from 13.8 to 42% for APSIM for all GCMs. Results showed that current and future cotton systems are adversely impacted by climate change; however, climate-change-adapted management approaches could offset possible reductions in productivity. Sustained cotton productivity in the core cotton zone requires capacity building amongst farmers, enabling them to improve their crop management in the face of seasonal climate variability and future climate change.

Integrating dual-purpose crops mitigates feedbase risk and facilitates improved lamb production systems across environments: a whole-farm modelling analysis

Context The winter feed gap is a common problem for livestock grazing systems worldwide, and changes to climate have made these deficits more unpredictable and extreme. Dual-purpose crops are an important tool in many southern Australian mixed crop–livestock systems to help fill the winter feed gap. Providing more reliable feed over winter can remove feed constraints and allow for earlier lambing in autumn with potential whole-farm system benefits. Aims We simulated a whole-farm livestock enterprise in the Agricultural Production Systems Simulator (APSIM) to examine the implications of spring- and autumn-lambing systems relying on a standard pasture-only feedbase compared with a farm where 25% of its grazed area is allocated to dual-purpose crops. Methods Twelve simulations were run across four locations in New South Wales, Australia, that varied in climatic conditions (both rainfall total and distribution) including two lambing systems (spring vs autumn) × two feedbase types (100% pasture vs 75% pasture and 25% dual-purpose crops) × three stocking densities. Key results For autumn-lambing systems, integrating dual-purpose crops helped to fill the winter feed gap and reduced supplement demand on average by ~28% compared with a pasture-only system. Compared with the standard pasture-only spring-lambing system, integrating dual-purpose crops into spring- and autumn-lambing systems more than doubled gross margin returns due to economic grain yield and lower supplement demand. A shift from spring- to autumn-lambing facilitated by dual-purpose crops also led to better reproductive performance of ewes in the subsequent year. In higher-rainfall, cooler environments, autumn-lambing systems with dual-purpose crops had the highest system gross margins, lowest economic risk and allowed for a safe increase in stocking density. In lower-rainfall, warmer environments, integration of dual-purpose crops into spring-lambing systems returned marginally higher gross margins than for the autumn-lambing system, but differences were less apparent at high stocking density. In lower-rainfall environments, dual-purpose crops helped to mitigate some of the economic risk, but the benefits were less clear. Conclusions We show dual-purpose crops can help fill the winter feed gap and support earlier lambing in autumn across a range of environments, especially in higher-rainfall cooler environments, with significant improvements in total farm gross margins. Implications Integrating dual-purpose crops will enable farmers to change their livestock system to mitigate their risks, reduce supplementary feeding and capitalise on other potential benefits, such as improved marketing and avoiding animal health problems.

Identification of environment similarities using a crop model to assist the cultivation and breeding of a new crop in a new region

Context Rainfed crop-growing environments are known for their high yield variability, especially in the subtropics and tropics. Improving the resilience of crops to such environments could be enhanced with breeding and agronomy research focusing on groups of similar environments. Aim This study presents a framework for developing these groups using the Agricultural Production Systems Simulator (APSIM, ver. 7.10) model. Methods As a case study, the framework was applied for pigeonpea (Cajanus cajan L. Millsp.) as a potential new pulse crop for the Australian northern grains region. The model was first validated and then used to simulate yield, compute heat and drought stress events and analyse their frequencies for 45 locations over 62 seasons from 1960 to 2021. Key results The model performed satisfactorily compared to field trial data for several sowing dates and locations. The simulated yield varied greatly across locations and seasons, with heat-stress events (maximum temperature ≥35°C) and rainfall showing highly significant associations with this variability. The study identified seven groups of locations after converting the simulated yield into percentiles, followed by clustering. Drought-and-heat stress patterns varied across these groups but less so within each group. Yield percentiles significantly declined over the seasons in three of the seven groups, likely due to changing climate. Conclusions The framework helped identify pigeonpea’s key production agroecological regions and the drought and heat constraints within each region. Implications The framework can be applied to other crops and regions to determine environmental similarity.

The Prediction of Wheat Yield in the North China Plain by Coupling Crop Model with Machine Learning Algorithms

The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha−1 and MAE = 498 kg ha−1). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month’s lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security.

Comparing CSM-CROPGRO and APSIM-OzCot Simulations for Cotton Production and Eddy Covariance-Based Evapotranspiration in Mississippi

Optimizing irrigation water use efficiency (WUE) is critical to reduce the dependency of irrigated cotton (Gossypium spp.) production on depleting aquifers. Cropping system models can integrate and synthesize data collected through experiments in the past and simulate management changes for enhancing WUE in agriculture. This study evaluated the simulation of cotton growth and evapotranspiration (ET) in a grower’s field using the CSM-CROPGRO-cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) and APSIM (Agricultural Production Systems simulator)-OzCot during 2017–2018 growing seasons. Crop ET was quantified using the eddy covariance (EC) method. Data collected in 2017 was used in calibrating the models and in 2018 validating. Over two cropping seasons, the simulated seedling emergence, flowering, and maturity dates were varied less than a week from measured for both models. Simulated leaf area index (LAI) varied from measured with the relative root mean squared errors (RRMSE) ranging between 20.6% to 38.7%. Daily ET deviated from EC estimates with root mean square errors (RMSEs) of 1.90 mm and 2.03 mm (RRMSEs of 63.1% and 54.8%) for the DSSAT and 1.95 mm and 2.17 mm (RRMSEs of 64.7% and 58.8%) for APSIM, during 2017 and 2018, respectively. Model performance varied with growing seasons, indicating improving ET simulation processes and long-term calibrations and validations are necessary for adapting the models for decision support in optimizing WUE in cotton cropping systems.

Optimization of sowing date and irrigation schedule of maize in different cropping systems by APSIM for realizing grain mechanical harvesting in the North China Plain

Grain mechanical harvesting can decrease the harvest cost and improve the efficiency of maize production. However, the optimization of the sowing date and irrigation schedule of maize in existing studies rarely considered the grain mechanical harvesting. Therefore, the sowing date and irrigation schedule of two maize cultivars in three cropping systems was optimized by considering yield, water use efficiency (WUE), and grain mechanical harvesting. The quality of grain mechanical harvesting is mainly influenced by grain moisture content. Thus, Agricultural Production Systems sIMulator (APSIM) was coupled with a grain dry down model to simulate maize production in the northern part of the North China Plain (NCP). The latest date for grain mechanical harvesting of maize was analyzed using the date when the grain moisture content decreased to 25% for the first time (DCM25). The DCM25, yield, WUE, and irrigation amount were used as constraints in the optimization. The results showed that, the optimal sowing dates under full irrigation of spring maize in SM (monocropping maize system), spring maize and summer maize in WMM (winter wheat-summer maize-spring maize system) for Huanong887 were from 6th May to 10th June, from 6th May to 16th May and 10th June, respectively. The optimal sowing dates of spring maize in SM were from 1st May to 5th June for Zhengdan958. The summer maize in DWM (winter wheat-summer maize double cropping system) could not achieve high-quality grain mechanical harvesting when using Huanong887 and Zhengdan958. In different hydrological year types, the optimal irrigation schedule with grain mechanical harvesting involved irrigation (80 mm each time) 1–3 times during sowing, jointing and flowering stages of spring maize in SM and summer maize in WMM. For spring maize in WMM, the optimal irrigation schedule involved irrigation 2–3 times during sowing, jointing, and flowering stages. The optimization method could provide a reference for other sites to promote high-efficient maize production.

Simulating water and nitrogen runoff with APSIM

To determine the impact of potential reductions of terrain-targeted nitrogen (N) fertilisation rates on N losses a simulation study was carried out using the Agricultural Production Systems Simulator (APSIM). To simulate N runoff a simple approach was used, in which runoff is based on the N concentration in the soil solution and an extraction coefficient. Firstly, APSIM parameters that have the largest effect on runoff of water and N were determined for terrains with different slopes for a poorly drained silt loam. A sensitivity analysis was then conducted to assess the effect of soil hydraulic properties and soil organic carbon content on runoff losses. Finally, APSIM was set up to simulate pasture production and water and N dynamics (including pasture N uptake, leaching and N runoff) for a farm on rolling hills in South Canterbury, New Zealand. Two different fertilisation approaches were used, either scheduled or based on the aboveground N concentration of the pasture. For the poorly drained silt loam, the rainfall intensity and the surface conductance had the highest effect on the amount of water lost by runoff. Soil hydraulic conductivity at saturation and field capacity, as well as plant available water content also controlled runoff of water and N, while the organic carbon content of the topsoil had less effect on N runoff. Both the extraction coefficient and the depth considered to exchange N with the runoff water affected the amount of N lost via runoff. Using the aboveground pasture N concentration prior to fertilisation had positive effects on pasture yield and reduced N runoff losses.

The effect of catch crops in spring barley on nitrate leaching and their fertilizer replacement value

Catch crops have been proven effective in reducing nitrogen leaching in cropping systems due to their uptake of post-harvest soil mineral N prior to the main percolation season in winter. Information on the residual effect of catch crops, and their nitrogen fertilizer replacement value is scarce. To determine nitrogen fertilizer replacement values for different catch crops a field experiment was conducted over a four-year period on a sandy soil in Denmark, involving different catch crops and catch crop mixtures with non-legume and legume species. The residual effect of the catch crops was measured in spring barley grown in the residual year. For comparison, winter rye was also included. Over the four-year period the spring barley/fodder radish showed significantly lower leaching compared with the other catch crops, and the winter rye had the highest leaching. Regarding the residual effect, fodder radish and ryegrass, were most valuable, with nitrogen fertilizer replacement values ranging from 13 to 41 kg ha-1, based on fertilizer recovery efficiency of 50%. The process based APSIM (Agricultural Production Systems sIMulator) model was used to simulate the spring barley/catch crop rotations and the winter rye, and showed good model performance regarding N leaching and, crop yield and N uptake. The model was then run over a period of 20 years, to determine variations in N leaching and residual effects due to environmental conditions. The simulations showed that catch crops reduced N leaching by 38–64% when grown every year, and by 21–39% when grown only every second year. Nitrogen fertilizer replacement values ranged on average from 28 to 44 kg N ha-1, but showed high inter-annual variability. The nitrogen fertilizer replacement values showed only a weak relationship with the N uptake of the catch crop prior to termination, likely due to long term effects of N mineralisation from catch crop residues and pre-emptive competition for N by the catch crop.