Agricultural Production Systems Simulator Research Articles

Component-wise iterative ensemble Kalman inversion for static Bayesian models with unknown measurement error covariance

The ensemble Kalman filter (EnKF) is a Monte Carlo approximation of the Kalman filter for high dimensional linear Gaussian state space models. EnKF methods have also been developed for parameter inference of static Bayesian models with a Gaussian likelihood, in a way that is analogous to likelihood tempering sequential Monte Carlo (SMC). These methods are commonly referred to as ensemble Kalman inversion (EKI). Unlike SMC, the inference from EKI is asymptotically biased if the likelihood is non-linear and/or non-Gaussian and if the priors are non-Gaussian. However, it is significantly faster to run. Currently, a large limitation of EKI methods is that the covariance of the measurement error is assumed to be fully known. We develop a new method, which we call component-wise iterative EKI (CW-IEKI), that allows elements of the covariance matrix to be inferred alongside the model parameters at negligible extra cost. This novel method is compared to SMC on a linear Gaussian example as well as four examples with non-linear dynamics (i.e. non-linear function of the model parameters). The non-linear examples include a set of population models applied to synthetic data, a model of nitrogen mineralisation in soil that is based on the Agricultural Production Systems Simulator, a model predicting seagrass decline due to stress from water temperature and light, and a model predicting coral calcification rates. On our examples, we find that CW-IEKI has relatively similar predictive performance to SMC, albeit with greater uncertainty, and it has a significantly faster run time.

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs

The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol’ method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs’ impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool.

I-RAT: A discussion support system to rapidly assess economic and environmental impacts of different sugarcane irrigation practices

Water pollution and climate change are among the greatest threats to the iconic Great Barrier Reef (GBR). To improve the GBR’s long-term outlook, making improvements on these fronts is required. Owing to the complex interactions between soils, climate, and farm management, a tool is needed to guide agricultural land managers about which management practice changes are more likely to deliver improved water quality and climate resilience outcomes whilst maintaining profitability. Using concepts drawn from social studies of science and technology, a ‘discussion’ support system (DSS) named Irrigation Rapid Assessment Tool (i-RAT) was developed through participatory processes designed to enhance co-learning from the development of this DSS. i-RAT is a rapid assessment-visualisation tool in an interactive web application (https://i-rat.net) to visualise the impacts of changed irrigation practices for sugarcane farmers and extension staff. Specifically, i-RAT compares these impacts regarding farm economics, water quality, productivity, carbon and nitrogen cycles, and greenhouse gas emission. The core of i-RAT is the sugarcane module from the Agricultural Production Systems sIMulator (APSIM) modelling platform. i-RAT was first developed for the Burdekin sugarcane growing region in Queensland (Australia’s largest sugarcane producing region). Various soil types and management scenarios representing farming practices in the Burdekin region were used for simulations (1971–2021) to generate a datacube. In this paper, we describe how fundamental learnings about more (drought) resilient farming systems and more sustainable irrigation practices can be extracted from i-RAT. Details of the participatory approach with research and industry partners that informed the design and function of i-RAT and how APSIM was set up and parameterised are described. A ‘what-if’ analysis demonstrated the i-RAT features and application.

Climate Change and Soil Dynamics: A Crop Modelling Approach

The impact of global climate change is a challenge to the sustainability of many ecosystems, including soil systems. However, the performance of soil properties under future climate was rarely assessed. Therefore, this study was carried out to evaluate selected soil processes under climate change using an agri-environmental modeling approach to Sri Lanka. The Agricultural Production Systems Simulator (APSIM) model was used to simulate soil and plant-related processes using recent past (1990–2019) and future (2041–2070) climates. Future climate data were obtained for a regional climate model (RCM) under representative concentrations pathway 4.5 scenarios. Rainfalls are going to be decreased in all the tested locations under future climate scenarios while the maximum temperature showcased rises. According to simulated results, the average yield reduction under climate change was 7.4%. The simulated nitrogen content in the storage organs of paddy declined in the locations (by 6.4–25.5%) as a reason for climate change. In general, extractable soil water relative to the permanent wilting point (total available water), infiltration, and biomass carbon lost to the atmosphere decreased while soil temperature increased in the future climate. This modeling approach provides a primary-level prediction of soil dynamics under climate change, which needs to be tested using fieldwork.

Genotype × environment × agronomic management interaction to enhance wheat yield in the Mediterranean rainfed environments of Morocco: II. Process based modeling

Durum wheat (Triticum turgidum subsp. durum) is the oldest and most cultivated cereal crop in Middle East and North Africa (MENA) region and under Mediterranean climatic conditions. Morocco is one of the largest producer of durum wheat in MENA region, cultivated in more than 1 million ha area produced 2.5 million tons in 2020, which accounts for 17% of the total production in the region. In the region, rainfed production system is predominant, and with declining rainfall amounts with high variability, increasing water scarcity, and suboptimal input application, its productivity growth is low and needs to be increased to fulfill the growing demand. Developing context-specific management advisory is needed to improve productivity and resilience under such variable rainfed production environments. Agricultural Production Systems sIMulator (APSIM) model was calibrated and evaluated using four years (2015–2019) of on-station experimental data from genotype × seeding time × water management experiment conducted at International Center for Agricultural Research in the Dry Areas (ICARDA) research station, Morocco. Long-term (1984–2021) simulation was carried out to determine the contribution of Genotype × Environment × Management components for sustainably improving crop productivity. The results showed rainfall or supplementary irrigation (23–36%) followed by N fertilizer (28–38%), cultivar (9–14%), and seeding date (7–14%) have the largest contribution to the yield variance of durum wheat in Merchouch, Meknes, and Sidi El Aidi regions of Morocco. Under rainfed conditions, wheat yield was highest in Merchouch (4.5 t ha−1) and lowest (1.8 t ha−1) in Sidi El Aidi. Due to significant rainfall variability, the seeding date varies across year and location; however, generally, it is between 2nd week of November to 1st week of December. Under rainfed conditions, seeding after 1st week of December caused the average yield reduction of 120, 81, and 31 kg ha−1 d−1 in Merchouch, Meknes, and Sidi El Aidi, respectively. In all locations, short-duration varieties provided higher averaged yields with better resilience than medium and long-duration varieties. Decomposing yield variance caused by Genotype × Environment × Management provides the opportunity for risk reduction, improvement of wheat yield and resilience, and designing climate-smart adaptation strategies in rainfed Mediterranean conditions. Our findings highlight one-size-fits-all approach is inadequate and context-specific tailored agronomic practices and suitable genotypes is crucial for achieving sustainability and resilience of wheat production in variable climatic condition in Morocco and similar production environment.

Modeling the effect of soil fertility management options on maize yield stability under variable climate in a sub-humid zone in Ghana

IntroductionUncertainty in the yield of maize due to variability in weather is a major challenge to smallholder farmers in Sub Sahara Africa. This study explores the potential of combining locally available organic resources and inorganic fertilizer to increase grain yield and reduce variability in yields associated with variations in rainfall distribution.MethodsTo assess the effectiveness of this practice, the Agricultural Production Systems sIMulator (APSIM) crop model was calibrated and evaluated using maize experiments on nutrient management options. The evaluated model was used to simulate maize growth and yield using multiple-year data (1984–2018) under different planting windows for two growing seasons. The treatments were (i) control, (ii) inorganic fertilizer alone (INOFRecom), combining organic resources [empty fruit bunch of palm (EFB) and compost with inorganic fertilizer (INOF) to make up equivalent nutrients in (ii)]; (iii) EFB + INOF and (iv) Comp + INOF.ResultsThough all the soil amendments boosted grain output, the EFB + INOF treatment outperformed the other treatments in the major season with gains of between 161 and 211% and the most stable (least inter-annual variability of 27%) yield. Across the planting windows, the INOFRecom and EFB + INOF treatments achieved comparable yield increments in the minor season. Though grain yield variability was high during the minor rainy season, combining organic and inorganic fertilizers reduced inter-annual yield variability, thus, lowering uncertainty in yield due to variable inter-annual rainfall. Combining local organic resources with a reduced amount of inorganic fertilizer produced higher yields and better yield stability compared to using only inorganic fertilizer.DiscussionsThus, such soil fertility management solutions might sustain resource use and boost maize grain yield in the study area, where strategies for sustainable crop nutrition remain a critical necessity. The improved nitrogen management regimes may result in fewer environmental hazards for vulnerable rainfed agricultural systems.

Predicting maize yield in Northeast China by a hybrid approach combining biophysical modelling and machine learning

ContextChina produces more than 20 % of maize grain in the world, and Northeast China (NEC) accounts for ∼30 % of the nation's total maize production. Previous studies have used either climate data, satellite data, or crop growth model (CGM) to predict or forecast maize yield. However, maize is highly susceptible to the effect of extreme climate events (such as drought, heat) in NEC, and there is a lack of studies to predict/forecast maize yield by integrating climate data, satellite data, extreme climate events, and CGM-simulated data. ObjectiveWe aim to develop a hybrid approach with machine learning to blend different sources of data (climate data, satellite data, extreme climate events) and process-based modelling results to improve predictive accuracy of maize yield in NEC. MethodsUsing maize data from 44 sites during the period of 2000–2013 in NEC, we firstly optimized Agricultural Production System sIMulator (APSIM) using Differential Evolution Adaptive Metropolis combined with Gaussian likelihood function and Bayesian multiplication method. Next, we divided the growing season into five phases, and selected variables of different phases using exploratory data analysis and Random Forest. Then, we developed a hybrid model using Random Forest by blending of multiple sources of data and APSIM simulations to predict maize yield from the start to the end of the growing season, and quantified the relative contribution of predictors. Results and conclusionsA hybrid model developed with random forest by combining climate data, NDVI, extreme climate events and APSIM simulations can achieve high performance for predicting yield toward the end of the growing season. The accuracy of in-season yield prediction showed a linear increase with MAPE/KGE changing from 19 %/0.05 to 13 %/0.53 from start to end of the growing season. Yield forecasts are acceptable with RMSE/MAE of 1.20/1.01 Mg ha−1 (16 %/13 % of the observed mean yield) approximately one-month prior to harvest. The most important predictor that affect yield forecast was APSIM-simulated biomass or yield, and the most important extreme climate event was drought during early grain-filling stage. SignificanceWith the increasing availability of crop-related data, we expect that the in-season forecasting capacity of the proposed methodology could be further improved, and the methodology can be extended to other crops and other regions for yield forecast.

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.

Short-term extreme heat at flowering amplifies the impacts of climate change on maize production

Extreme weather poses a threat to global crop production, food security and farmer livelihoods. High temperatures have been identified as detrimental to crop yields; however, how heat stress during the critical flowering stage will influence future maize (Zea mays L.) yields remains unclear. Here, we combined statistical and process-based models to assess impacts of short-term extreme heat at flowering on Chinese maize yield under climate change. We showed that heat around flowering has a stronger impact on yields than heat at other times in the growing season, especially for temperatures >30 °C. Heat stress during flowering was responsible for 23% of total yield loss from extreme degree days (EDDs) in 1990–2012. An improved process-based model (Agricultural Production Systems sIMulator (APSIM)-maize) incorporating a grain-temperature function was then applied and indicated that extreme heat at flowering amplified the impacts of climate change on maize production compared to the original model. The improved APSIM-maize predicted an 8.7% yield reduction across the Chinese Maize Belt as EDDs increased more than quadrupled at the end of the century (2070–2099) under a high emissions pathway (SSP585) in comparison with the baseline period (1990–2019). Our study highlights the importance of extreme heat at flowering on maize yield and can inform farmers and policy makers on adaptive measures as well as providing a reference for other crop areas facing similar challenges.

Effect of soil water on flowering and pod-set in chickpea: implications for modelling and managing frost and heat stress

Phenological development is critical for crop adaptation. Phenology models are typically driven by temperature and photoperiod, but chickpea phenology is also modulated by soil water, which is not captured in these models. This study is aimed at evaluating the hypotheses that accounting for soil water improves (i) the prediction of flowering, pod-set, and flowering-to-pod-set interval in chickpea and (ii) the computation of yield-reducing frost and heat events after flowering. To test these hypotheses, we compared three variants of the Agricultural Production System Simulator (APSIM): (i) APSIMc, which models development with no temperature threshold for pod-set; (ii) APSIMx, which sets a threshold of 15 °C for pod-set; and (iii) APSIMw, derived from APSIMc with an algorithm to moderate the developmental rate as a function of soil water, in addition to temperature and photoperiod common to all three models. Comparison of modelled and actual flowering and pod-set of a common cheque cultivar PBA BoundaryA in 54 diverse environments showed that accuracy and precision were superior for APSIMw. Because of improved prediction of flowering and pod-set timing, APSIMw improved the computation of the frequency of post-flowering frosts compared to APSIMc and APSIMx. The number of heat events was similar for all three models. We conclude that accounting for water effects on plant development can allow better matching between phenology and environment.

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.

Simulating Gibberellic Acid Effect on Pasture Yield on Naturally Deposited and Fixed Area Urine

Nitrate (NO3−-N) leaching from urine patches is a serious environmental concern in dairy pastoral systems. In our previous research, we established that application of a plant growth hormone, gibberellic acid (GA), can potentially reduce NO3−-N leaching in urine patches; however, this was investigated in two locations in New Zealand. The performance of GA in influencing pasture nitrogen (N) uptake and NO3−-N leaching needs to be undertaken in multi-locations to draw conclusions. However, multi-location studies are a challenge due to a lack of funding and time constraints, so models such as the agricultural production systems simulator (APSIM) have been used. Therefore, field studies were conducted to determine whether APSIM can be used to quantify and simulate the effect of GA on NO3−-N leaching and pasture yield in three experimental sites known as Ashburton, Stratford, and Rotorua in New Zealand. Treatments examined were control (no urine applied), urine at 600 kg N ha−1, urine + GA at 8 g ha−1. The observed data was used to calibrate and validate the model. APSIM simulated that application of GA reduced NO3−-N leaching (relative to urine treatment) by 4.6, 5.1, and 8.8 kg NO3−-N ha−1 in Ashburton, Stratford, and Rotorua, respectively. APSIM reliably simulated pasture dry matter yield, and this was confirmed by the coefficient of determination ranging from R2 = 0.8562 to 0.995 in all treatments and experimental sites. This study demonstrated that APSIM can effectively be used to simulate the effect of GA application on NO3−-N leaching and pasture yield. Therefore, APSIM can be applied in other areas to simulate NO3−-N leaching and pasture yield.

APSIM next generation mungbean model: A tool for advancing mungbean production

Mungbean, a grain legume with high nutritional value, is grown widely throughout Asia and increasingly in Australia. Despite growing interest amongst farmers, mungbean remains an inconsistent and thus risky crop to plant in Australia. Cropping system models like the Agricultural Production Systems sIMulator (APSIM) are valuable tools for helping farmers to examine options for improving crop management and assess production risks across potential growing regions for mungbean. This paper outlines the simulation capacity of a new mungbean crop model parameterized using the Plant Modelling Framework in APSIM Next Generation, the newest version of the APSIM framework. The aim of the paper is to document the parameterization and validation processes of the model. The new mungbean model was built using data from 28 field experiments to simulate measured phenology, canopy development, biomass accumulation/partitioning, stress responses, N fixation, root growth, and yield across a wide range of environments. The root mean squared error (RMSE) in predictions for grain weight and aboveground weight were 25.4 g m−2 and 91.4 g m−2, respectively. The model successfully captured the dynamics of crop response to sowing dates, water/irrigation regimes, and climate. The new mungbean model is a robust and accurate tool for use in Australia and tropical/sub-tropical Asia. Researchers can use the new mungbean model to determine best management practices such as the optimal time to sow mungbeans in different environments. The output from model simulations can help farmers assess risks associated with sowing at different times and soil water conditions specific to their region. Such risk analysis can improve farmer decision-making confidence in mungbean, increasing its potential production for Australia. Overall, the new APSIM mungbean model can be used effectively to identify and close the mungbean yield gap, mitigate risk of crop failure, and increase profits for mungbean farmers in Australia and tropical/sub-tropical Asia; it has the capacity to assist with increasing mungbean production globally under changing climate conditions.

Maize planting date and maturity in the US central Great Plains: Exploring windows for maximizing yields

Region-specific guidelines for maize in the US Great Plains are needed to account for variability in farmer practices and inherent soil and climate differences. Therefore, it becomes critical to define environment tailored management strategies for maize crop to improve and sustain yields over time. The aims of this study were to: i) define homogenous maize yield environments classified as related to the maximum yield and its stability over time in the US central Great Plains region; ii) explore the combinations of planting date and hybrid maturity that lead to optimal yield for each of the previously defined homogenous maize yield environments; and iii) identify environments presenting different windows for maximizing yields (combinations of planting date by hybrid maturity). Hybrid maturity (growth cycle) by planting date combinations were explored at 70 sites within central and eastern Kansas and northern Oklahoma (herein defined as the US central Great Plains region) using 30 years of historical weather data. In addition to the analysis of an extensive maize field dataset, a crop growth model, Agricultural Production Systems Simulator (APSIM), was utilized to evaluate the crop yield and phenology of all hybrid maturity by planting date combinations to define environment tailored management strategies. The simulated yields were spatially clustered based on attainable yield and stability over time to delimit more homogeneous productivity environments. Overall, greater yields were attained with long-maturing hybrids (comparative relative maturity, CRM, 101 and 115). Delaying the planting date from April to mid-May decreased yield in eastern environments. Moreover, late planting maintained yields and increased stability in central environments. For central Kansas and Oklahoma, anticipation of physiological maturity date (3–16 days) with reduced yield penalties (<5%) were possible by using short-season hybrid maturities (CRM 85). Future scenarios for intensification could be explored in those environments presenting more windows for maximizing yields, either with early planting and harvest dates or via late planting providing time for a short crop before maize. Adoption of more region-specific guidelines for maize management could help to optimize yield and its stability over time and seek further opportunities to diversify risk and intensify our current maize-based farming systems in the US central Great Plains.

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.

Long-term measurement and modelling of net herbage accumulation in grazed pastures do not align with predictions under climate change

A long-term phosphorus (P) fertiliser and sheep grazing experiment at the AgResearch Research Station at Ballantrae (1975-2022) provides a unique resource to examine long-term changes in net herbage accumulation (NHA) and animal production under conditions where soil P fertility have been non-limiting for more than 35 years. This paper examined historical NHA, soil phosphorus (P) and nitrogen (N) fertility from the high fertiliser (HF) farmlet, and pasture growth trends using the climate driven pasture growth module AgPasture within the Agricultural Production Systems Simulator (APSIM). On the medium slope of the HF farmlet, NHA in 2020-21 was only 87% of that measured on the same farmlet between 1982-88, even though P was non-limiting. The measured decline in NHA aligned with a reduction of on-site nominal sheep stocking rates since the late 1990’s. Prior to this paper, climate driven modelling has often predicted a likely positive outcome in NHA for this environment into the future. Understanding the apparent discrepancy between predictions into the future with what is happening on the ground today is discussed in the paper.

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.

An optimal combination of sowing date and cultivar could mitigate the impact of simultaneous heat and drought on rainfed wheat in arid regions

Drought and heat stress are the most important limiting factors for crop production. Simultaneous impacts of drought and heat stress during crop’s sensitive growth phases could be more harmful than their individual impacts. Accordingly, we investigated several management practices including 4 sowing dates, 3 cultivars in combination with 4 initial soil water contents to investigate the simultaneous impact of drought and heat stress on rainfed wheat grain yield in different climates (57 locations) across Iran from 1980 to 2016. A modified version of the wheat module of the Agricultural Production Systems sIMulator (APSIM) modelling framework was adopted. Historical temporal trends showed that grain yield has increased by + 10 kg ha-1 y-1 in cold regions and decreased by 14.5 kg ha-1 y-1 in mild and hot regions since 1980 for Azar-2 (as common cultivar). Long-term simulations indicated that grain yields of 3.3 t ha-1 could be obtained in rainfed agro-ecosystems under only-drought growth conditions. By adding the impact of heat stress, however, average grain yield further declined to 2.9 t ha-1 indicating a reduction of 0.4 t ha-1 owing to heat stress per se for the common cultivar. The results also showed that the long-term impact of drought and heat stress could be diminished with an optimal combination of sowing date, cultivar, and initial soil water depending upon climate classes. A combination of an early-maturity cultivar (“Azar-2”) with early sowing date (07-Oct) was recognized as the best management practice for cold and mild regions while the interaction of a mid-maturity cultivar (MMC), and an intermediate sowing date (20-Oct) was the best management practice for hot climates.

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

Different types of soil data are used in process-based crop models as input data. Crop models have a diverse range of applications, and soil research is one of them. This bibliographic analysis was conducted to assess the current literature on soil-related applications of crop models using two widely used crop models: Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT). The publications available in the Scopus database during the 2000–2021 period were assessed. Using 523 publications, a database on the application of process-based crop models in soil research was developed and published in an online repository, which is helpful in determining the specific application in different geographic locations. Soil-related applications on APSIM and DSSAT models were found in 41 and 43 countries, respectively. It was reported that selected crop models were used in soil water, physical properties, greenhouse gas emissions, N leaching, nutrient dynamics, and other physical and chemical properties related to applications. It can be concluded that a crop model is a promising tool for assessing a diverse range of soil-related processes in different geographic regions.

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.