Agricultural Production Systems Simulator Crop Research Articles

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

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

Characterizing spatial and temporal trends in drought patterns of rainfed wheat (Triticum aestivum L.) across various climatic conditions: A modelling approach

Drought is considered one of the most common and challenging environmental stresses in the agricultural production worldwide. For improving the rainfed crop yields in the drought-prone environments, understanding the seasonal patterns of drought events in the target environments is crucial. This study applies the APSIM (Agricultural Production Systems sIMulator) crop model to interpret drought stress impact on a check genotype (early-maturity genotype) and mid- and late-maturity genotypes in 69 sites and 16 climate classes with climate data from over 37 years representing the rainfed wheat agro-ecosystems in Iran. Four drought patterns were recognized across the 16 regions. Water stress was dominant during grain filling (41 %) and around flowering (56 %) in studying regions. This stress varied over different genotypes and climate classes. Furthermore, results showed that under current management practices and using the check genotype, about half of the regions in the rainfed wheat agro-ecosystems of Iran (∼1.7 million ha) are frequently subject to severe drought patterns (50 %) resulting in a loss of grain yield of ∼2 t ha−1. Replacing a common genotype with an extra early-maturity genotype, however, reduced by 44 % the occurrence of severe drought patterns which simultaneously increased the occurrence of light-moderate drought stress (i.e. DP1 and DP2) in the warm regions. In general, planting the extra early-maturity genotype had the advantage of producing higher yields in the driest regions. Sowing this genotype reduced the variability in grain yield and the frequency of severe drought patterns in the warm regions. On the contrary, in rainy and cold regions, the best-yielding genotypes were those with a longer growth period. Therefore, in rainy and cold regions with light-moderate stress conditions, the breeders should focus on early-maturity and mid-maturity genotypes. Besides, it is suggested that the late-maturity genotype should be used in rainy regions with a reduced risk of drought stress.

A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models

As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the permanent wilting point (PWP) of Sri Lankan soils) for process-based crop models. We used the Agricultural Production Systems sIMulator (APSIM) as the test model. Initially, we confirmed the importance of PWP (LL15) and FC (DUL) by assessing the sensitivity of the soil input parameters on the growth and yield of rice under rainfed conditions. We simulated the growth and yield of rice and the four selected outputs related to the APSIM soil module using the measured and estimated values of FC and PWP. These simulations were conducted for ten years in 16 locations of Sri Lanka, representing wet, intermediate, and dry zones. The simulated total aboveground dry matter and weight of the rough rice, using both input conditions (the measured and PTF-estimated soil hydraulic properties), showed good agreement, with no significant differences between each other. Outputs related to the soil module also showed good agreement, as no significant differences were found between the two input conditions (measured and PTF-estimated soil hydraulic properties). Although the DUL and LL15 are the most influential parameters for the selected outputs of APSIM–Oryza, the estimated FC and PWP values did not change the predictive ability of APSIM. In this way, the functionality of PTFs for APSIM crop modeling is confirmed.

Drought risk assessment of spring maize based on APSIM crop model in Liaoning province, China

Drought risk assessment is a vital part of drought risk management, which plays an important role in drought mitigation. Due to its complexity, drought risk is difficult to define and challenging to quantitatively assess, as the drought impacts associate with many social sectors. This contribution method the issue by quantitatively evaluating the yield loss due to drought as a function of the drought severity indicator in Liaoning province, China for spring maize using logarithmic regression. As crop water deficit is essence to identify agricultural drought, it developed a drought severity indicator using the crop water stress coefficient and duration. The Agricultural Production Systems sIMulator (APSIM) crop model was employed to simulate the spring maize growth to obtain daily water deficit during the growth period (May to September) and yield. The relationship between drought severity frequency and yield loss rate due to drought was established to assess the drought risk of spring maize when drought severity frequency is equal to 20%, 10%, 5% and 2%. The results show that Chaoyang and Fuxin have the highest drought risk in four levels of drought severity frequency whilst the lowest drought risk was identified in Tieling. The central Liaoning province has a moderate drought risk. For a specific drought severity frequency, drought risk increases from east to west in Liaoning province whilst it varies in each city at different drought severities. This method can predict yield loss due to drought for drought early warning. Drought risk maps presents spatial characteristics that can help to agricultural drought mitigation and the development of drought preparedness plan in Liaoning province.

Optimal Nitrogen Rate Can Be Predicted Using Average Yield and Estimates of Soil Water and Leaf Nitrogen with Infield Experimentation

Core Ideas Nitrogen fertilizer decisions can be improved when on‐farm experimentation is combined with historical yields and the sensing of crop N and soil water status. Crop simulation and machine learning can be combined to gain insight into the key drivers that influence an N decision. In spring wheat crop, an N‐minus strip can provide advanced warning of an impending N stress. ABSTRACTCrop nitrogen (N) fertilizer decision aids traditionally provide an N fertilizer recommendation that requires information about the supply of N from the soil and the likely demand of N by the crop. These are often complex to implement. To simplify, we take elements of multiple N decision philosophies and, by way of simulation and machine learning, determine which variables need to be estimated to inform N decisions for rainfed wheat that are near optimal. Simulations were conducted using the APSIM (Agricultural Production Systems Simulator) crop wheat model for sites across the Australian grain belt for fields containing test strips where 0 or an additional 80 kg N ha−1 of fertilizer was added. The random forest analysis of 59 separate soil and plant variables within the 1.2 million simulations demonstrated that the long‐term mean yield was the most important variable to predict the optimal N rate for a particular site and season. When long‐term mean yield was used to predict the optimal N regime for a given season alone, the RMSE of the predicted N rate was 53.9 kg N ha−1. The RMSE declined to 21.8 kg N ha−1 when additional information was included in the Random Forest analysis. Leaf N concentration and soil water status to depth enhanced the N prediction, especially when this information was collected from an infield N experiment with an N‐minus, standard, and N‐rich strip of N.

Effect of Future Climate Change on Wheat Yield and Water Use Efficiency Under Semi-arid Conditions as Predicted by APSIM-Wheat Model

Any change in environmental conditions will affect crop growth and development and have an effect on crop productivity. The objective of the current study was to investigate the effect of climate change on irrigated wheat production and water use efficiency in Fars province in Iran. Accordingly, a general circulation model (HadCM3) was applied for two emission scenarios (A1B and A2) for three periods (2011–30, 2046–65 and 2080–2099) at nine locations in Fars province in central Iran. The APSIM (Agricultural Production Systems sIMulator) crop model was used to simulate growth and development of wheat as well as water use efficiency under future climate scenarios. The results indicated that the increase in CO2 concentration to 674 ppm in 2099 under A1B neutralized the negative effects of high temperature during the growing season and improved crop yield. The results indicate that, by the end of the century under the A2 emission scenario 10–15% of Fars province will have a grain yield of more than 10 t ha−1 and about 65% will have a grain yield of 8–10 t ha−1. Averaged across locations, scenarios and periods, water use efficiency increased by 3.56 kg ha−1 mm−1 in the future scenarios over baseline. Overall, the improved water use efficiency under future climate change was largely the result of a significant increase in yield (from 6989.5 kg ha−1 at baseline to 8416.5 kg ha−1 in all future scenarios) and decreased evapotranspiration (from 506.8 mm at baseline to 478 mm in all future scenarios).

Adaptation strategies to lessen negative impact of climate change on grain maize under hot climatic conditions: A model-based assessment

Rise in temperature, particularly in warmer areas, could have a negative effect on maize productivity. However, careful management practices could reduce the effects of high temperatures by altering the sowing dates and type of cultivar. The current study applied APSIM (The Agricultural Production Systems sIMulator) crop model to investigate the interaction of sowing date and cultivar when dealing with climate change and high temperatures at nine locations in Khuzestan province, in southwestern Iran. Daily climatic data for the baseline period of 1980–2010 was obtained from the Meteorological Organization of Iran. Projections of the future climate of Khuzestan was done in Miroc5 (Model for Interdisciplinary Research on Climate) GCM (Global Climate Model) for 2040–2070 under two RCPs (Representative Concentration Pathways) (RCP4.5 and RCP8.5) using the methodology developed by AgMIP (The Agricultural Model Intercomparison and Improvement Project). The high risk window for extreme temperature was calculated as the number of days having a maximum temperature of over 36 °C (Tmax > 36 °C) during pre-flowering and flowering. Results indicated that for mid-future (2050), the average maize grain yield in almost all study areas except Masjed Soleyman decreased in comparison to baseline at −13.7% and −22.8% for RCP4.5 and RCP8.5, respectively mainly because the length of the high-risk window for extreme temperature had expanded from 18.8 to 26.3 days for RCP4.5 and RCP8.5, respectively, compared to baseline. Most farmers have not realized that they are currently sowing maize during a high-risk window for extreme temperatures (Tmax > 36 °C) in some seasons. If farmers do not apply adaptive options for their regions (most promising sowing date × cultivar), the probability of economical grain yield will be less than 50% for an average economical grain yield of 8.9 t ha−1. The current findings support the hypothesis that climate change by the middle of the 21st century will not be beneficial for maize agroecosystems in hot areas like Khuzestan province unless the best sowing date × cultivar is applied for both winter and summer sowing dates.

Canola as a New Crop for California: A Simulation Study

Core Ideas The accuracy of the Agricultural Production Systems Simulator crop model for simulating canola production in California was tested. The crop model accurately predicted canola yields across the state. Simulations support observations from multi‐environments trials that canola has both high mean yields and yield potential in California. The simulation results suggest canola is a viable alternative crop for diversifying cool‐season annual cropping in California. Canola could maintain economically viable yields under climate change scenarios projected for the region. The agricultural sector of California is one of the most economically valuable and diverse in the world, but is dominated by perennial tree, vine and forage crops, as well as warm‐season annual species that are dependent on irrigation. The diversity of less water‐intensive annual cool‐season crops is limited and wheat (Triticum aestivum L.) is the primary crop. Canola (Brassica napus L.) could diversify annual cool‐season cropping in the state. Our study used field data from a multi‐environment canola variety trial to test the ability of the Agricultural Production Systems Simulator (APSIM) model to simulate canola production in California. APSIM was able to accurately simulate canola yields in diverse regions, and consequently was used to investigate the yield potential of canola in California, using different irrigation management strategies, under both current and anticipated future climate scenarios. These simulations predict that canola should have high mean yields throughout California, given suitable management and variety selection. The long‐term mean yield for short‐season, spring‐type, canola in the central valley of California is predicted to be more than 4800 kg/ha with supplemental irrigation. Under rain‐fed conditions in the northern central valley mean yields are predicted to be 3500 kg/ha. This should make canola economically competitive with cool‐season cereals. Without additional improvements in variety adaptation or management changes, our simulations suggest the yield of canola in California will decline modestly, but remain economically viable, under future climate scenarios.

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

AbstractSimulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re‐parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re‐parameterized APSIM modules. The re‐parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.

Integrating pest population models with biophysical crop models to better represent the farming system

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework.

Considering effective adaptation options to impacts of climate change for maize production in Ghana

As a contribution to guide policy planning, effective climate change adaptation options to enhance maize production in Ghana were examined. The Agricultural Production Systems Simulator crop model, coupled with statistical downscaled climate scenarios of 9 general circulation models (GCMs) and farmer practices, were used to device adaptation options in the near future (NF) period of 2046–2065 based on IPCC A2 emission scenario at local scale level in Wa and Wenchi agricultural areas of Ghana. Results from the single-maize cropping season at Wa, in the savanna zone indicate that, a 6-week delay in sowing from 1961 to 2000 baseline date of 1st May to 15th June, was the most appropriate way to offset the negative effects of potential climate change resulting in a yield increase of 8.2%. In the forest-savanna transitional zone of Wenchi, a 4-week delay in the major season sowing date from 15th March to 15th April and no change in the minor season sowing date of 15th August resulted in a slight yield increase of 3.9%. On the other hand, a second adaptation option of using a medium heat-tolerant maize variety produced substantial gains in crop yield of 30.1% and 30.5% in savanna and forest-savanna transitional zones respectively with a longer grain-formation period under NF climate change when compared to delaying sowing dates. Effective adaptation options can thus reduce negative impacts of climate change in the NF, but require enabling policy to support its implementation.

Opportunities and trade-offs in dual-purpose cereals across the southern Australian mixed-farming zone: a modelling study

Dual-purpose cereals are employed in the high-rainfall zone of southern Australia to provide additional winter forage. Recently there has been interest in applying this technology in the drier environments of South and Western Australia. It would therefore be useful to gain an understanding of the trade-offs and risks associated with grazing wheat crops in different locations. In this study the APSIM (Agricultural Production Systems Simulator) crop and soil simulation models were linked to the GRAZPLAN pasture and livestock models and used to examine the benefits and costs of grazing cereal crops at 21 locations spanning seven of the regions participating in the Grain & Graze research, development and extension program. A self-contained part of a mixed farm (an annual pasture–wheat rotation plus permanent pastures) supporting a breeding ewe enterprise was simulated. At each location the consequences were examined of: (i) replacing a spring wheat cultivar with a dual-purpose cultivar (cv. Wedgetail or Tennant) in 1 year of the rotation; and (ii) either grazing that crop in winter, or leaving it ungrazed. The frequency of early sowing opportunities enabling the use of a dual-purpose cultivar was high. When left ungrazed the dual-purpose cultivars yielded less grain on average (by 0.1–0.9 t/ha) than spring cultivars in Western Australia and the Eyre Peninsula but more (by 0.25–0.8 t/ha) in south-eastern Australia. Stocking rate and hence animal production per ha could be increased proportionately more when a dual-purpose cultivar was used for grazing; because of the adjustments to stocking rates, grazing of the wheat had little effect on lamb sale weights. Across locations, the relative reduction in wheat yield caused by grazing the wheats was proportional to the grazing pressure upon them. Any economic advantage of moving to a dual-purpose system is likely to arise mainly from the benefit to livestock production in Western Australia, but primarily from grain production in south-eastern Australia (including the Mallee region). Between years, the relationship between increased livestock production and decreased grain yield from grazing crops shifts widely; it may therefore be possible to identify flexible grazing rules that optimise this trade-off.