Agricultural Production Systems Simulator Model Research Articles

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.

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.

Investigating the effects of APSIM model configuration on model outputs across different environments

IntroductionSoil type plays a major role in nutrient dynamics and soil water which impacts crop growth and yield. The influence of soil characteristics on crop growth is usually evaluated through field experimentation (in the short term) and through crop-soil modelling (in the long-term). However, there has been limited research which has looked at the effect of model structural uncertainty of model outputs in different soil types.MethodsTo analyze the impact of soil inputs on model structural uncertainty, we developed eight model structures (a combination of two crop models, two soil water models and two irrigation models) within the Agricultural Production Systems sIMulator (APSIM) across three soil types (Ferralsols, Alisols and Chernozems). By decomposing the mean proportion of variance and simulated values of the model outputs (yield, irrigation, drainage, nitrogen leaching and partial gross margin) we identified the influence of soil type on the magnitude of model structural uncertainty.ResultsFor all soil types, crop model was the most significant source of structural uncertainty, contributing >60% to variability for most modelled variables, except irrigation demand which was dominated by the choice of irrigation model applied. Relative to first order interactions, there were minimal (<12%) contributions to uncertainty from the second order interactions (i.e., inter-model components). We found that a higher mean proportion of variance does not necessarily imply a high magnitude of uncertainty in actual values. Despite the significant impact of the choice of crop model on yield and PGM variance (contributing over 90%), the small standard deviations in simulated yield (ranging from 0.2 to 1 t ha-1) and PGM (ranging from 50.6 to 374.4 USD ha-1) compared to the mean values (yield: 14.6 t ha-1, PGM: 4901 USD ha-1) indicate relatively low actual uncertainty in the values. Similarly, the choice of irrigation model had a contribution of over 45% to variance, but the relatively small standard deviations ranging from 11 to 33.3 mm compared to the overall mean irrigation of 500 mm suggest low actual uncertainty in the values. In contrast, for the environmental variables- drainage and nitrogen leaching, the choice of crop model had contributions of more than 60% and 70% respectively, yet the relatively large standard deviations ranging from 7.1 to 30.6 mm and 0.6 to 7.7 kg ha-1 respectively, compared to the overall mean values of drainage (44.4 mm) and nitrogen leaching (3.2 kg ha-1), indicate significant actual uncertainty.DiscussionWe identified the need to include not only fractional variance of model uncertainty, but also magnitude of the contribution in measured units (e.g. t ha-1, mm, kg ha-1, USD ha-1) for crop model uncertainty assessments to provide more useful agronomic or policy decision-making information. The findings of this study highlight the sensitivity of agricultural models to the impacts of moisture availability, suggesting that it is important to give more attention to structural uncertainty when modelling dry/wet conditions depending on the output analyzed.

Assessing the effect of using different APSIM model configurations on model outputs

The three major sources of uncertainty in crop models are model inputs, structure and parameters. Model structure is one of the major contributors to this uncertainty, however, its quantification is difficult due to limitations in controlling confounding effects from parameter and input uncertainty. The objective of this study was to quantify the contribution of structural uncertainty to the variance in model outputs produced by the Agricultural Production Systems sIMulator (APSIM). Outputs investigated were yield, irrigation requirements, partial gross margin, drainage and nitrogen (N) leaching. Eight model structures differing in choice of soil water model, crop model and irrigation model were developed within a single APSIM version (v.7.10) and tested under three contrasting environments (climate × soil) across 120 years. We quantified: (i) the model structure uncertainty (from soil water, crop and irrigation models) using analysis of variance (ANOVA) and deviation analysis; and (ii) the variability of outputs due to model structure and climate using the coefficient of variation. Confounding effects from inputs, parameters and model users were controlled. Most structural uncertainty resulted from first order effects of the choice of model components (crop model: 12.2–98.9%, irrigation model: 0–78.4%, soil water model:1–33.7%) rather than second order interactions between components (0.1–18.9%). Furthermore, uncertainty from choice of sub-model/model used was not necessarily related to the structural complexity of these components. The effects of structural uncertainty on predictions commonly used to inform agronomic, ecological or policy decision making were strongly impacted by site and climate conditions i.e., high rainfall site (∼1330 mm year−1) had less uncertainty and variability as compared to low rainfall site (∼610 mm year−1), highlighting the need for any uncertainty assessment to cover the entire range of conditions for model application. Here we show the value of a component-based modelling framework for quantifying uncertainty in crop modelling studies.

Field and in-silico analysis of harvest index variability in maize silage.

Maize silage is a key component of feed rations in dairy systems due to its high forage and grain yield, water use efficiency, and energy content. However, maize silage nutritive value can be compromised by in-season changes during crop development due to changes in plant partitioning between grain and other biomass fractions. The partitioning to grain (harvest index, HI) is affected by the interactions between genotype (G) × environment (E) × management (M). Thus, modelling tools could assist in accurately predicting changes during the in-season crop partitioning and composition and, from these, the HI of maize silage. Our objectives were to (i) identify the main drivers of grain yield and HI variability, (ii) calibrate the Agricultural Production Systems Simulator (APSIM) to estimate crop growth, development, and plant partitioning using detailed experimental field data, and (iii) explore the main sources of HI variance in a wide range of G × E × M combinations. Nitrogen (N) rates, sowing date, harvest date, plant density, irrigation rates, and genotype data were used from four field experiments to assess the main drivers of HI variability and to calibrate the maize crop module in APSIM. Then, the model was run for a complete range of G × E × M combinations across 50 years. Experimental data demonstrated that the main drivers of observed HI variability were genotype and water status. The model accurately simulated phenology [leaf number and canopy green cover; Concordance Correlation Coefficient (CCC)=0.79-0.97, and Root Mean Square Percentage Error (RMSPE)=13%] and crop growth (total aboveground biomass, grain + cob, leaf, and stover weight; CCC=0.86-0.94 and RMSPE=23-39%). In addition, for HI, CCC was high (0.78) with an RMSPE of 12%. The long-term scenario analysis exercise showed that genotype and N rate contributed to 44% and 36% of the HI variance. Our study demonstrated that APSIM is a suitable tool to estimate maize HI as one potential proxy of silage quality. The calibrated APSIM model can now be used to compare the inter-annual variability of HI for maize forage crops based on G × E × M interactions. Therefore, the model provides new knowledge to (potentially) improve maize silage nutritive value and aid genotype selection and harvest timing decision-making.

Identifying optimum residue levels for stable crop and water productivity and carbon sequestration under a conservation agriculture based rice-wheat system

To sustain food-production targets while reducing residue burning in the Indo-Gangetic Plains, adoption of conservation agriculture (CA) is desirable, amongst other potential adaptations. To identify the optimum residue levels for sustainable CA, the Agricultural Production Systems Simulator (APSIM) model was used to analyse 37 years (1984–2022) of diverse CA scenarios on productivity, sustainability and carbon footprints in the rice-wheat cropping system (RWCS). The study highlighted that APSIM has the capacity to capture the crop performance, phenology and CA scenario in RWCS. The scenario analysis indicated that maximum system productivity (SP) was recorded under higher residue (HR-9.33 t ha−1) followed by higher-medium residue (HMR-8.97 t ha−1) scenarios of CA, as compared to conventional tillage (CT-8.75 t ha−1). Stable productivity was achieved under CA. Sustainable yield index and sustainable value index were significantly higher under HR (0.83 and 0.79, respectively) followed by HMR. The soil organic carbon concentration is predicted to increase ∼30–95% in CA with a carbon sequestration rate of 0.1–0.37 t ha−1 yr−1. The system water productivity was highest under HR (3.68 kg ha−1 mm−1) which was ∼10% higher than CT. Overall, the study revealed that the APSIM model is efficient in capturing CA effects in South Asian RWCS and that the adoption of CA results in greater and stable yields, higher water productivity, and more carbon capture over the long term, while reducing production costs.

Biomass production of a sub-tropical grass under different photovoltaic installations using different grazing strategies

CONTEXTWith increasing population growth and land-use competition, pasture production under photovoltaic installations offers an alternative paradigm for crop-livestock integration. The levels of pasture biomass production and potential for livestock grazing under photovoltaic installations depend considerably on the mode of the photovoltaic system. OBJECTIVEThis study aimed to model pasture production for sub-tropical grass under different photovoltaic installations and assess the effects of different grazing methods on sub-tropical pasture productivity in Australia. METHODSPasture biomass production under a fixed-tilt array, single-axis tracking array, and dual-axis tracking array were measured for the calibration and validation of Agricultural Production Systems sIMulator (APSIM) to simulate four different grazing strategies: (1) without grazing; (2) 21 days grazing interval; (3) 45 days grazing interval; and (4) continuous stocking. RESULTS AND CONCLUSIONSThe APSIM model showed satisfactory performance in simulating sub-tropical pasture production under different photovoltaic installations, with the best correspondence under the fixed-tilt array (observed value 6073 kg ha−1 and simulated value 6292 kg ha−1). As compared to full sun condition, biomass production was found to be 15.82, 13.53, and 8.03% higher with the fixed-tilt, single-axis tracking, and dual-axis tracking array, respectively. The model was then used in scenario analysis to evaluate pasture biomass production under different grazing strategies. Simulation results depict the grazing effect on pastures under the photovoltaic systems. Without grazing, maximum biomass production occurred under the fixed-tilt array (7798 kg ha−1) compared to under the single-axis tracking array (7671 kg ha−1), dual-axis tracking array (7186 kg ha−1), and full sun (6766 kg ha−1). In the case of 21 days and 45 days of grazing, the 25-year biomass production under the full sun was lower than all other systems. Compared to other treatments, the fixed titled array offers better performing pasture-grazing integration under a photovoltaic system. We found that models predicted that an increase in grazing pressure via continuous grazing had comparatively similar impacts on sub-tropical pasture biomass production irrespective of photovoltaic installations. Therefore, the potential exists to maximise land use efficiency where options are available to grow and graze pasture under photovoltaic installations. SIGNIFICANCEThis study confirms that the APSIM-Growth model, calibrated for Bambatsi Panic, can simulate pasture growth in different shading phenomena under the agrivoltaic system. Additionally, this simulation of the grazing systems is essential to identify crucial modelling and direct investigations that could expand knowledge of the procedures and relationships required for model development of pasture production under photovoltaic farms.

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.

Simulations of solar-induced chlorophyll fluorescence over crop canopies using the integrated APSIM model

Agricultural production models predict crop yield by accounting for a variety of species, cultivar, farming management, and environmental impacts on crop photosynthesis. Without suitable constraints, however, large uncertainties may exist in simulations of crop photosynthesis. Recent advances in retrieving solar-induced chlorophyll fluorescence (SIF) at the top-of-canopy (TOC) have provided a promising measurement for crop photosynthesis. Within the framework of the APSIM (Agricultural Production Systems sIMulator) model, a SIF module was developed to connect crop photosynthesis to TOC SIF emission (SIFtoc) which can be measured by remote sensing platforms. The new model (APSIM-SIF) first estimates the leaf-level chlorophyll fluorescence emitted over the full SIF spectrum (SIFtot_full) according to CO2 assimilation in crops. The model then mechanistically decomposes the conversion from SIFtot_full to SIFtoc into two factors: the SIF band conversion factor (ɛ) and the fluorescence escape ratio (fesc) that represent the impact of leaf physiological status and plant structure properties, respectively. ɛ can be estimated using leaf structural and biochemical parameters as inputs; fesc for near-infrared SIF can be expressed as a function of directional reflectance in the near-infrared region (RNIR), Normalized Difference Vegetation Index (NDVI), and the fraction of PAR absorbed by crops (fAPAR). The APSIM-SIF model determined more than 90% of the variation in gross primary productivity (GPP), aboveground biomass and leaf area index (LAI) measurements for maize (Zea mays L.) at two AmeriFlux sites in the U.S. Midwest and it also captured the seasonality of SIF (R2 = 0.84) and GPP (R2 = 0.81) well at an irrigated maize site in China. The APSIM-SIF model was also applied to the simulation of TOC SIF emission of maize and soybean (Glycine max L.) in the U.S. Midwest during the 2018 growing season. The simulated SIFtoc accounted for more than 75% of the variability of daily satellite SIF observations for grid squares with more than 70% crop area. The main contribution of this study lies in two aspects: (1) a physically-based framework is proposed to incorporate the SIF module to the APSIM-DCaPST model, and (2) the two important factors used in this framework (ɛ and fesc) remains largely constant during the peak growing season. These findings provide a theoretically robust and operational basis for linking SIF observations with crop growth.

Augmenting agroecosystem models with remote sensing data and machine learning increases overall estimates of nitrate-nitrogen leaching

Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

A forage brassica simulation model using APSIM: Model calibration and validation across multiple environments

Forage brassicas have historically been used in high rainfall/irrigated temperate livestock systems, but there is increasing interest in diverse forage brassicas in drier mixed crop-livestock farming systems. Computer-based modelling is an important decision support tool used in agriculture to explore the adaptability of crops to different climates and agronomic management practices, but existing modelling tools for forage brassicas are limited to temperate environments. We parameterised the APSIM (Agricultural Production Systems Simulator) model for four forage brassica genotypes, including three diverse forage rape cultivars and a raphanobrassica. The model was calibrated using two experiments with repeated measures of biomass components, nutritive value, and leaf and canopy development. We then tested the model extensively using data from a diverse set of environments within Australian and New Zealand (23 sites across four agro-climatic zones). Model predictions of biomass were good for all the genotypes (NSE > 0.60, Nash-Sutcliffe efficiency; RMSE ~1.5 t DM/ha, root mean square error). Predictions of metabolisable energy yield were satisfactory for all genotypes (NSE 0.43–0.73; RMSE ~17.8 GJ ME/ha) but forage dry matter digestibility (DMD) were poorly predicted due to the small variation in observed data. Our robust and widely tested model can be confidently used to predict forage productivity of common and new forage brassicas across a wide range of production environments and agronomic management practices. This model will enable future work to develop a better understanding of the potential value of these important forage crops for livestock production systems.

Yields and Nitrogen Dynamics in Ley-Arable Systems—Comparing Different Approaches in the APSIM Model

Nitrogen (N) dynamics in ley-arable cropping systems require better understanding in order to assess the potential of such systems to contribute to improved productivity and reduced nutrient losses in crop production. Large inputs of organic matter after termination of the ley phase result in increased mineralization and N availability to subsequent crops. The description and quantification of this residual N effect in ley-arable systems remains a major scientific challenge due to its variability and many influencing factors. Simulation modeling could contribute to improved understanding of N dynamics in ley-arable systems. The aim of this study was to evaluate the robustness of the Agricultural Production Systems Simulator (APSIM) to predict biomass yield, N yield, and N leaching of different forage maize systems in northwest Europe, while using two different approaches to predict the residual N effect. The evaluation was based on three field experiments covering plant phenology, biomass, N yield, and N leaching over several years. Model adjustments were necessary to describe mineralization of organic matter and release of N after ploughing of the grass leys. For this purpose, three scenarios were investigated by accounting for either (1) aboveground grass residues; (2) above- and belowground grass residues, both with the generic turnover approach in the model; or (3) N release depending on the carbon-to-N ratio of the residue compiled in a simple mineralization model (SMM). The results showed that APSIM-simulated biomass and N yield of maize were reasonable to poor across the different systems and sites, regardless of using the residue-related approach. The SMM performed more accurately compared to the generic turnover approach in predicting N leaching in a maize following a grass-clover ley. However, for all scenarios, APSIM had difficulties to predict a delay of N leaching observed in the experimental data after a pure ryegrass ley. In conclusion, the process description in APSIM related to organic matter mineralization in ley-arable systems under northwest European pedo-climatic conditions needs improved accounting of belowground grass residues, while the SMM is of added value to improve N mineralization patterns and leaching after a ley phase.

An APSIM-powered framework for post-rainy sorghum-system design in India

Sorghum contributes to the livelihoods of millions of food-insecure households in semi-arid agri-systems. Annual production widely fluctuates throughout India due to erratic rainfall and suboptimal agronomic practices. Our novel approach utilizes the digital reflection of post-rainy (rabi) sorghum production systems in India to help better understand its spatio-temporal variations and enable the designing of geography-specific, climate-responsive system interventions (Genotype × Management; GxM). For this, we evaluated a range of farmer-relevant agronomic management practices across three soil types (shallow, medium, and deep vertisols) in combination with observed ranges of biological variability in sorghum cultivar characteristics. We used the crop growth simulation model Agricultural Production Systems sIMulator (APSIM) to identify GxM combinations that can support the enhancement/ stability of post-rainy sorghum production systems in India. In general, we found the post-rainy sorghum systems would benefit from early-season sowing (16th - 23rd September), short crop duration (compared to Maldandi (M35–1), commonly grown crop type), and medium fertilizer inputs (70–70 kg urea ha −1 as basal and top-dress application). In addition, site-specific crop management (M) and crop characters (G) optimizations would further enhance/ stabilize sorghum production. Simulations highlighted that in the poorly-endowed environmental context (i.e. shallow soils and low-rainfall areas), optimal G×M targets might involve water conservation GxM combinations, such as low plant populations and low fertilization along with low crop vigor and limited transpiration responsiveness. Details on site-specific optimum GxM are available in a web application at https://ls40.pef.czu.cz/maps/. To enable the use of the study outputs for certain applications (e.g. breeding), we separated the examined geographies based on similarities in optimum production characteristics and similarities in system response to GxM interventions into four “homogeneous system units” (HSU; i.e. geographical units within which reduced GxM interactions are expected). These HSUs intended to offer geography-specific targets to prioritize, test, and introduce distinct G×M interventions. We conclude that the APSIM-powered framework presented provides region-specific Genotype × Management options that could become a blueprint for defining quantitative breeding targets that achieve enhanced productivity/ stability of dry-season sorghum cultivation throughout India. • APSIM-driven framework to capture the spatio-temporal variations in sorghum production was established. • This framework quantified the effects of sorghum genetics and management (GxM) on crop production. • Site-specific GxM optimization would increase stover and grain production over the current practices. • GxM options to optimize sorghum production in targets geographies were defined. • The APSIM-framework is available on-line at https://ls40.pef.czu.cz/maps/.

Improving early-season wheat yield forecasts driven by probabilistic seasonal climate forecasts

Seasonal climate forecasts (SCF) are evolving rapidly alongside improvements in climate modelling and downscaling research, and have great potential for weather-sensitive sectors, especially agriculture, by reducing weather-related risks and increasing productivity. Skilful yield forecasts at the beginning of, or before, a cropping season can provide farmers and other stakeholders in agribusiness with the necessary information for early planning and actions. Only a few yield forecast studies have a forecast lead time of four months or longer due to the problem complexity. To enable SCFs from Global Climate Models (GCMs) to be used for early-season yield forecasts, this paper uses a statistical downscaling technique, Extended Copula Post-Processing (ECPP) and the Schaake shuffle, to downscale four climate variables to generate weather-like daily data that are suitable for agricultural applications. Climate forecasts drive a process-based crop model APSIM (Agricultural Production Systems sIMulator) to simulate crop forecasts on 50 stations, well-distributed across the Australian grain zone. To focus on yield forecast skills attributable to SCF, we propose best practice management rules to predict water-limited winter wheat yield. Yield forecasts from ECPP have a significant improvement over quantile mapping downscaling and raw SCF from the Australian recent seasonal forecast model ACCESS-S1 in terms of bias, accuracy, reliability, and overall forecast skill. In addition, even at the beginning of a cropping season with a forecast lead time of four or more months, yield forecasts driven by ECPP illustrate higher skill than climatology, a benchmark for yield forecast. Early-season yield forecasts driven by SCFs provides a promising alternative to regression/machine-learning-based forecasts. Performance sensitivity and issues, and gaps on using skilful SCFs to help growers with their farming decision-making are discussed.

Spatial Assessment of Jerusalem Artichoke’s Potential as an Energy Crop in the Marginal Land of the Shaanxi Province, China

As a foodstuff crop, Jerusalem artichoke has a promising prospect for providing sustainable feed-stock sources for bioenergy development. Due to relatively limited cultivated land resources in China, it is crucial to evaluate Jerusalem artichoke’s potential production capacity in marginal land. Based on Jerusalem artichoke’s growth and photosynthetic characteristics, the agricultural production systems simulator model (APSIM) and multi-factor integrated assessment method were integrated to provide an operational method for comprehensively evaluating the marginal land resources suitable for developing the plantation of Jerusalem artichoke in the Shaanxi province, China. The results showed that 0.73 million ha of marginal land was suitable for Jerusalem artichoke cultivation in the Shaanxi province, and 5.4 million ha of marginal land was fairly suitable for Jerusalem artichoke cultivation, with the yield reaching 44,289 kg/ha and 38,861 kg/ha, respectively. The suitable land resources are mainly located in Yan’an (0.18 million ha), Hanzhong (0.13 million ha), and Baoji (0.08 million ha), most of which are moderate dense grassland (accounting for 50.6% of suitable land), dense grassland (accounting for 16.2% of suitable land), shrubland (accounting for 14.7% of suitable land), and sparse forest land (accounting for 9.18% of suitable land). The findings of this study can be used to establish targeted policies for Jerusalem artichoke development in China and other countries, particularly those along the Silk Road.

Wheat (Triticum aestivum) adaptability evaluation in a semi-arid region of Central Morocco using APSIM model

In this study, we evaluated the suitability of semi-arid region of Central Morocco for wheat production using Agricultural Production Systems sIMulator (APSIM) considering weather, soil properties and crop management production factors. Model calibration was carried out using data collected from field trials. A quantitative statistics, i.e., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and index of agreement (d) were used in model performance evaluation. Furthermore, series of simulations were performed to simulate the future scenarios of wheat productivity based on climate projection; the optimum sowing date under water deficit condition and selection of appropriate wheat varieties. The study showed that the performance of the model was fairly accurate as judged by having RMSE = 0.13, NSE = 0.95, and d = 0.98. The realization of future climate data projection and their integration into the APSIM model allowed us to obtain future scenarios of wheat yield that vary between 0 and 2.33 t/ha throughout the study period. The simulated result confirmed that the yield obtained from plots seeded between 25 October and 25 November was higher than that of sown until 05 January. From the several varieties tested, Hartog, Sunstate, Wollaroi, Batten and Sapphire were yielded comparatively higher than the locale variety Marzak. In conclusion, APSIM-Wheat model could be used as a promising tool to identify the best management practices such as determining the sowing date and selection of crop variety based on the length of the crop cycle for adapting and mitigating climate change.

Does the APSIM model capture soil phosphorus dynamics? A case study with Vertisols

• Soil P dynamics in Vertisols are not well understood. • Crop productivity in this sub-tropical region is mostly driven by soil water availability. • APSIM Soil-P parameters attribution lacks guidance. • Labile-P simulation relies on choice of the “rate loss available” coefficient. • More mechanistic understanding required to better understand soil P dynamics. Crop production in the tropics and sub-tropics increasingly relies on phosphorus (P) fertilisers to maintain crop yields. As a result, there is a need to better understand the effectiveness of P management strategies, especially in Vertisols that represent a large proportion of the cropped area. The complexity of soil P dynamics and the interaction with environmental factors and farming practices make this challenging but the use of agricultural systems models such as the Agricultural Production Systems sIMulator (APSIM) can potentially improve our understanding of these interactions. This study (1) gives the state of knowledge of P behaviour in Vertisols and identifies which soil P processes are simulated in the APSIM Soil-P module, (2) tests the ability of APSIM as a tool to simulate soil P dynamics and crop responses in two long-term P studies in Vertisols, and (3) discusses future work needed to improve modelling of soil P dynamics. Soil P dynamics in Vertisols are not well understood, but evidence suggests that precipitation and dissolution of calcium-P minerals may play a significant role in plant P availability. These characteristics are substantially different to the strongly P-sorbing soils on which the APSIM model has previously been tested, so the ability of the model to simulate soil P dynamics and crop responses on these soils is untested. The APSIM model was able to predict the variation in crop yields due to seasonal variation of moisture availability within an acceptable modelling performance but was less able to predict P responses within seasons. This weakness was consistent with the lack of linear correlation between the plant-available P pools predicted by the P module and the measured Colwell-P that was correlated with crop responses. The non-linear relationships also differed between the two Vertisols. The poor understanding of P dynamics in Vertisols and the inability to partition soil P into measurable P pools is hindering the development of a mechanistic P module. The resulting lack of an appropriate mechanistic approach to soil P dynamics questions the predictive ability of the model. Further fundamental work is needed to predict the fate of applied P fertilisers in soils with contrasting physical and chemical characteristics, with this information integrated into an improved P modelling framework.

A process-based model reveals the restoration gap of degraded grasslands in Inner Mongolian steppe

Due to the influence of climate change and extensive grazing, a large proportion of steppe grassland has been degraded worldwide. The Chinese government initiated a series of grassland restoration programs to reverse the degradation. However, the limiting factors and the restoration potential remain unknown. Here we present a process-based model to assess the restoration gap (RG) defined as maximum biomass differences between non-degraded and degraded grasslands with different degrees of soil and vegetation degradation. The process-based model Agricultural Production Systems Simulator (APSIM) was evaluated utilizing observation data from both typical and meadow steppes under natural conditions in terms of phenology, dynamics of above-ground biomass and soil water content. Scenario analysis and sensitivity analysis were subsequently performed to address the RG and controlling factors during 1969–2018. The results showed that the calibrated model performed well with r > 0.75 and model efficiency factor EF > 0.5 for all the simulation components. According to our model results, the RG was larger in typical steppe compared to that of meadow steppe and it increased with increasing soil and/or vegetation degradation, to ~60% under extremely degraded scenarios. Both soil and vegetation degradation led to reduced water use efficiency, with an elevated proportion of soil evaporation to evapotranspiration (Es/ET), however, the limiting factor for RG varied. The degradation of soil water holding capacity contributed more to RG regardless of climate conditions for typical steppe in all years and for meadow steppe in dry years. In wet years the importance of vegetation coverage reduction increased for RG in meadow steppe, where the relative importance of vegetation coverage (valued at 62.8%) was 25.6% higher than that of soil degradation. Our results demonstrated the importance of considering climate variations when developing protection and restoration programs for grassland ecosystems.

Simulations of the Soil Evaporation and Crop Transpiration Beneath a Maize Crop Canopy in a Humid Area

Soil evaporation (Es) and crop transpiration (Tc) are important components of water balance in cropping systems. Comparing the accurate calculation by crop models of Es and Tc to the measured evaporation and transpiration has significant advances to the optimal configuration of water resource and evaluation of the accuracy of crop models in estimating water consumption. To evaluate the adaptation of APSIM (Agricultural Production Systems simulator) in calculating the Es and Tc in Nanjing, APSIM model parameters, including the meteorological and soil parameters, were measured from a two-year field experiment. The results showed that: (1) The simulated evaporation was basically consistent with the measured Es, and the regulated model can effectively present the field evaporation in the whole maize growth period (R2 = 0.85, D = 0.96, p < 0.001); and (2) The trend of the simulated Tc can present the actual Tc variation, but the accuracy was not as high as the evaporation (R2 = 0.74, D = 0.87, p < 0.001), therefore, the simulation of water balance process by APSIM will be helpful in calculating Es and Tc in a humid area of Nanjing, and its application also could predict the production of maize fields in Nanjing.

Simulation of the Optimum Planting Windows for Early and Intermediate-Maturing Maize Varieties in the Nigerian Savannas Using the APSIM Model

The Agricultural Production Systems Simulator (APSIM) model was calibrated and validated and used to identify the optimum planting windows for two contrasting maize varieties for three agro-ecologies in the Nigeria savannas. The model was run for 11 planting windows starting from June 1 and repeated every 7 days until 16 August using long-term historical weather data from the 7 selected sites representing three agro-ecological zones (AEZs). The evaluation with the experimental data showed that the model performance was reasonable and accurately predict crop phenology, total dry matter (TDM) and grain yield for both maize varieties. The seasonal planting date analysis showed that optimum planting windows for 2009EVDT and IWDC2SynF2 depend on the variety, agro-ecozones and sites. Planting from June 15 to 28 simulated the highest mean grain yield for both varieties in all the agro-ecologies. In the Southern Guinea savanna (SGS) where the length of growing season is 180–210 days, the best planting window was June 8–July 19 for 2009EVDT and June 8–July 26 for IWDC2SynF2 in Abuja. The planting window that gives attainable yield at Yelwa, is June 15–July 5 for 2009EVDT and June 8–28 for IWDC2SynF2. In the Northern Guinea savannah (NGS) where the length of growing season is 150–180 days, the optimum planting window is June 15–July 19 for both varieties at Zaria and June 8–July 19 for 2009EVDT and June 8–August 2 for IWDC2SynF2 at Sabon Gari. In the Sudan savannah (SS) where the growing season is 90–120 days, planting of 2009EVDT can be delayed up to the third week of July. For the medium-maturing variety, IWDC2SynF2, planting should be done by the first week of July. Though Yelwa is in the SGS, lower yields and narrower sowing windows were simulated for both varieties than for those of the other locations. This is probably due to the poor soil fertility in this location.