Agricultural Production Systems Simulator Research Articles

APSIM – Evolution towards a new generation of agricultural systems simulation

Agricultural systems models worldwide are increasingly being used to explore options and solutions for the food security, climate change adaptation and mitigation and carbon trading problem domains. APSIM (Agricultural Production Systems sIMulator) is one such model that continues to be applied and adapted to this challenging research agenda. From its inception twenty years ago, APSIM has evolved into a framework containing many of the key models required to explore changes in agricultural landscapes with capability ranging from simulation of gene expression through to multi-field farms and beyond.Keating et al. (2003) described many of the fundamental attributes of APSIM in detail. Much has changed in the last decade, and the APSIM community has been exploring novel scientific domains and utilising software developments in social media, web and mobile applications to provide simulation tools adapted to new demands.This paper updates the earlier work by Keating et al. (2003) and chronicles the changing external challenges and opportunities being placed on APSIM during the last decade. It also explores and discusses how APSIM has been evolving to a “next generation” framework with improved features and capabilities that allow its use in many diverse topics.

Can models help to forecast rainwater dynamics for rainfed ecosystem?

Simulation models are important tools to explore and illustrate dynamics of climatic variables in crop based ecosystem. In the rainfed ecosystem (RE), wheat production is impinged on certain climatic events per se high variability in rainfall and increased temperature. These climatic events turn out due to climatic drivers like Sea Surface Temperatures (SSTs) and pressure. Current study is aimed to analyze long term rainfall data (1961–2011) of Pakistan׳s rainfed ecosystem zone (Islamabad, Chakwal and Talagang) by using Agricultural Production Systems Simulator (APSIM) and R model. The principal objective of this analysis was to study the link between SOI phases and SSTs; and thereby understanding the pattern of climate change due to these climatic drivers under rainfed conditions in Pakistan. The results revealed a positive link between July SOI phases and the rainfall variability during October–November (the sowing time of wheat in Pakistan). Long term rainfall data analysis (1961–2011) of Islamabad, Chakwal and Talagang revealed 44%, 40%, 35% possibility of exceeding median rainfall near zero whereas probability of consistently negative SOI phases were 35%, 34% and 33% respectively during July. Similarly, the forecasting results estimated by R using covariates like dry spell, NINO1.2, NINO3, NINO4, NINO3.4 and IOD of different months revealed that prediction of monsoon, wheat early growth, wheat grain filling period and total wheat growing season rainfall, have significant signals with climatic drivers. The study justified the importance of models in the decision making processes and rainfall forecasting as a beneficial and necessary tool for rainfed ecosystem conservation.

Development of an oil palm cropping systems model: Lessons learned and future directions

Oil palm has become one of the most important crops in the world with questions being raised about its economic and environmental sustainability. Agricultural systems models are regularly employed in studying sustainable crop management but no detailed model is currently available for oil palm systems.We developed a production systems model for oil palm within the Agricultural Production Systems Simulator (APSIM) framework and tested it using data across a range of environments within Papua New Guinea (PNG). The model captured key growth responses to climate and management. This demonstrates that modern modelling frameworks do allow for rapid model development for new agricultural systems.However, whilst application of the model is promising, the availability of key data is likely to restrict its use. Local soil and weather data are not available in adequate detail for many of the major oil palm production areas, although some methods exist to address this.

Soil Carbon Sequestration Potential as Affected by Management Practices in Northern China: A Simulation Study

Soil has been identified as a possible carbon (C) sink for sequestering atmospheric carbon dioxide (CO2). However, soil organic carbon (SOC) dynamics in agro-ecosystems is affected by complex interactions of various factors including climate, soil and agricultural management practices, which hinders our understanding of the underlying mechanisms. The objectives of this study were to use the Agricultural Production Systems sIMulator (APSIM) model to simulate the long-term SOC dynamics under different management practices at four long-term experimental sites, Zhengzhou and Xuzhou with double cropping systems and Gongzhuling and Ürümqi with single cropping systems, located in northern China. Firstly, the model was calibrated using information from the sites and literature, and its performance to predict crop growth and SOC dynamics was examined. The calibrated model was then used to assess the impacts of different management practices, including fertilizer application, irrigation, and residue retention, on C dynamics in the top 30 cm of the soil by scenario modelling. Results indicate a significant SOC sequestration potential through improved management practices of nitrogen (N) fertilizer application, stubble retention, and irrigation. Optimal N fertilization (Nopt) and 100% stubble retention (R100) increased SOC by about 11.2%, 208.29%, and 283.67% under irrigation at Gongzhuling, Zhengzhou, and Xuzhou, respectively. Soil organic carbon decreased rapidly at Ürümqi under irrigation, which was due to the enhanced decomposition by increased soil moisture. Under rainfed condition, SOC remained at a higher level. The combination of Nopt and R100 increased SOC by about 0.46% under rainfed condition at Ürümqi. Generally, agricultural soils with double cropping systems (Zhengzhou and Xuzhou) showed a greater potential to sequester C than those with single cropping systems (Gongzhuling and Ürümqi).

Crop sequences in Australia’s northern grain zone are less agronomically efficient than implied by the sum of their parts

The question addressed in this paper is whether the agronomic efficiency of cropping sequences can be improved in the northern grain zone of Australia. Analysis of whole systems over multiple seasons is particularly important in cropping regions such as Australia’s northern grain zone, where farmers have the option of growing a variety of winter and summer crops and where fallowing is required to store water to safeguard yields of following crops. The study is based on a detailed longitudinal survey of the inputs and yields of 94 farmers’ fields over 7 seasons. The Agricultural Production Systems Simulator (APSIM) was used to benchmark the 94 cropping sequences and to determine if the productivity of these cropping sequences can be improved through changed management, particularly of nitrogen nutrition and timely sowing of individual crops. Comparison of surveyed and simulated yields were then used to determine the yield gap that can be explained by factors unaccounted for in APSIM, i.e. biotic stresses such as weeds, pests and diseases or inadequate operational management. Using a generalised N response curve, data from the simulation analysis were plotted relative to a normalised input–output relationship to determine their position relative to an efficiency frontier representing the simulated production function. This analysis was conducted separately for both individual crops and for whole sequences in individual fields. The analysis of 193 individual crops showed that the average yield of 1.90t/ha was 65% of the simulated water and nitrogen-limited yield potential of 2.92t/ha. Investigation of nitrogen unlimited and early sowing management options revealed that there was relatively little yield advantage (0.15t/ha) to be gained from these management improvements. The remaining gap between observed yields and what the simulations suggest may be attributed to factors such as weeds, pests and diseases, sub-optimal operations or extreme weather events. Three production criteria were used to compare cropping sequences: (1) dollar income, (2) metabolisable energy yield, and (3) crude protein yields. While the income from 36% of the individual crops in the study was found to be more than 80% of their production frontier values, only 29% of whole cropping sequences achieved this benchmark. Similar results were achieved when crops and crop sequences were evaluated in terms of their metabolisable energy and crude protein yields. It is concluded that in order to increase the agronomic efficiency of cropping in Australia’s northern grain zone, attention should be focussed on the intensity and configuration of cropping sequences and on the management of fallows in addition to the management of individual crops.

Applicability of APSIM to capture the effectiveness of irrigation management decisions in rice-based cropping sequence in the Upper-Gangetic Plains of India

Rice–wheat (RW) production system, which covers over 13.5 million ha in the Indo-Gangetic Plains of south Asia, is vital for food and nutritional security and livelihood of millions of poor people in this part of the region. Availability of irrigation water under projected climate change scenarios is a great concern, and demonstration of the impact of different irrigation regimes on rice, wheat, and system yields is essential to adopt suitable water saving technologies to minimize risk. This study tested the ability of the agricultural production systems simulator (APSIM) model to simulate the effects of different irrigation regimes on yield, irrigation water requirement, and irrigation water productivity (WPi) of rice, wheat, and RW system in upper-gangetic plains of India. The long-term simulated rice yield showed a steadily declining trend at an average rate of 120 kg ha−1 yr−1 (R 2 = 0.94, p < 0.05), while long-term simulated wheat yields showed a lower declining trend at an average rate of 48 kg ha−1 yr−1 (R 2 = 0.48, p < 0.05). The highest WPi of 8.31 kg ha−1 mm−1 was observed under RW system with the rice irrigation (IR) regime of 8 days alternate wetting and drying (AWD) and five irrigations for wheat with a yield penalty of 25.5 %. The next highest WPi was observed in the treatment with a 5-day AWD regime in rice and five irrigations for wheat, with a yield penalty of 20.1 %. Thus, we can suggest that a 5-day AWD irrigation regime for rice combined with five irrigations during wheat could be the best option under water limiting situations.

Defining agro-ecological regions for field crops in variable target production environments: A case study on mungbean in the northern grains region of Australia

The northern grains region (NGR) of Australia, which includes the state of Queensland and the northern half of the New South Wales, has highly variable climate leading to heightened production risk for all rainfed crops. Characterisation of the production environment of this region can assist in exploration of potential opportunities for reducing this risk. In this case study on mungbean (Vigna radiata L. Wilczek.) we demonstrate how this region could be characterised using the Agricultural Production Systems sIMulator (APSIM) model. The model was first evaluated for variety Crystal grown widely in the region, and then applied to simulate a water stress index (the daily supply and demand ratio) and yield from 1889 to 2012 at 28 locations. The model was run using location specific as well as three generic soils of 136, 166 and 204mm plant available water holding capacities (PAWC). Two complementary characterisations were performed using the simulated output, one based on clustering of supply demand ratio averaged for every 100°Cd to and from flowering, and another on clustering of percentile rankings of seasonal yield variation at different locations. Clustering of supply demand ratio revealed four drought patterns (i.e., target production environments) which commenced at different times from flowering. Seasonal frequencies of these drought patterns, which differed due to major location effects and relatively smaller soil effects, accounted for significant (∼84%) variation in simulated yield. Clustering of percentile ranks corresponding to simulated yield in different seasons identified seven meaningful yield clusters. Location memberships of these yield clusters were geographically contiguous and were only slightly influenced for the lowest PAWC generic soil. All locations within these yield clusters showed a tendency to have similar seasonal drought patterns and their frequencies. Locations within different yield clusters could therefore be considered as part of distinct agro-ecoregions. These model defined agro-ecoregions could be used as selection environments for their dominant target production environment(s) to develop new genotypes and their agronomy for better adaptation and yield under variable climatic conditions.

Evaluating APSIM Maize, Soil Water, Soil Nitrogen, Manure, and Soil Temperature Modules in the Midwestern United States

The Agricultural Production Systems sIMulator (APSIM) is a cropping systems modeling platform that is used worldwide to address research questions related to agricultural systems. We explored whether APSIM performs well in the Midwest, so that the associated model capabilities would be available for application in this region. Our approach included calibration and testing of several APSIM models (maize [Zea mays L.], soil water, soil N, surface organic matter, manure, and soil temperature) and evaluation of model predictions against independent datasets. During calibration we developed local crop and soil parameters so that the model captured sufficiently well dynamics of soil water (root mean square error, RMSE = 0.032 mm mm−1), soil temperature (RMSE = 2.1°C), soil inorganic N dynamics (RMSE = 12.6 kg N ha−1), contrasting soil net N mineralization patterns under fresh and composted swine manure applications (RMSE = 13.6 kg N ha−1), crop phenology (RMSE = 1.52 d), leaf area index (RMSE = 0.60 m2 m−2), leaf N concentration (RMSE = 0.28 kg 100 kg−1), canopy N uptake (RMSE = 9.0 kg N ha−1), biomass production (RMSE = 0.77 Mg ha−1) and grain yield (RMSE = 0.53 Mg ha−1). The calibration protocol followed in this study is discussed in detail. The calibrated model was evaluated against independent data on grain yield (RMSE = 0.65 Mg ha−1), biomass production (RMSE = 1.1 Mg ha−1) and LAI (RMSE = 1.14 m2 m−2), showing very acceptable performance, especially in addressing yield‐N relationships. Therefore APSIM proves to be a reliable model that can be used as a research and decision tool to enhance Midwestern production systems.

Managing crop stubble during fallow period for soil water conservation: field experiment and modelling

In the arid and semi-arid environments where the rainfall is limited and variable, fallow period soil moisture conservation using stubble is one of the ways of increasing the soil moisture required for crop sowing and development. However, the effectiveness of moisture conservation using stubble depends on the paddock management, soil water content, and rainfall characteristics. To assess the effect of stubble rate and amount of rainfall on the soil moisture conservation, a two-season field experiment was conducted using four stubble rates (0, 2, 4, 6 t/ha) and two water supply amounts. The soil water dynamics was also analysed using a validated Agricultural Production System Simulator (APSIM). In the relatively wet summer season with a high initial soil water content, the amount of water stored in the 2, 4, and 6 t/ha stubble rate treatment plots was higher than that of the bare plots by 10.4, 15.9, and 17.8 mm, respectively. However, in the summer season with a relatively high amount of in-season water input and low initial soil water content, the soil water storage was increased by 29.4, 35.6, and 43.0 mm, respectively. Comparing the results of the two seasons, the highest increase was observed for the 2 t/ha stubble rate. The amount of conserved soil moisture was found to be the highest when the soil profile water content at the start of the summer-fallow period is low and the amount of rain during the summer season is high. The good agreement between the measured and APSIM-simulated soil water contents (R 2 = 0.812), indicates that the model can be used to assess the soil water dynamics under a fallow condition. The APSIM-simulated soil water balance using the weather data of the past 100 years indicated that in a year with low start-of-fallow period soil water content, a 6 t/ha stubble rate can increase the end-of-fallow period soil moisture by up to 60 %.

Influence of management and environment on Australian wheat: information for sustainable intensification and closing yield gaps

In the future, agriculture will need to produce more, from less land, more sustainably. But currently, in many places, actual crop yields are below those attainable. We quantified the ability for agricultural management to increase wheat yields across 179 Mha of potentially arable land in Australia. Using the Agricultural Production Systems Simulator (APSIM), we simulated the impact on wheat yield of 225 fertilization and residue management scenarios at a high spatial, temporal, and agronomic resolution from 1900 to 2010. The influence of management and environmental variables on wheat yield was then assessed using Spearman’s non-parametric correlation test with bootstrapping. While residue management showed little correlation, fertilization strongly increased wheat yield up to around 100 kg N ha−1 yr−1. However, this effect was highly dependent on the key environment variables of rainfall, temperature, and soil water holding capacity. The influence of fertilization on yield was stronger in cooler, wetter climates, and in soils with greater water holding capacity. We conclude that the effectiveness of management intensification to increase wheat yield is highly dependent upon local climate and soil conditions. We provide context-specific information on the yield benefits of fertilization to support adaptive agronomic decision-making and contribute to the closure of yield gaps. We also suggest that future assessments consider the economic and environmental sustainability of management intensification for closing yield gaps.

Sustainable limits to crop residue harvest for bioenergy: maintaining soil carbon in Australia's agricultural lands

AbstractThe use of crop residues for bioenergy production needs to be carefully assessed because of the potential negative impact on the level of soil organic carbon (SOC) stocks. The impact varies with environmental conditions and crop management practices and needs to be considered when harvesting the residue for bioenergy productions. Here, we defined the sustainable harvest limits as the maximum rates that do not diminish SOC and quantified sustainable harvest limits for wheat residue across Australia's agricultural lands. We divided the study area into 9432 climate‐soil (CS) units and simulated the dynamics of SOC in a continuous wheat cropping system over 122 years (1889 – 2010) using the Agricultural Production Systems sIMulator (APSIM). We simulated management practices including six fertilization rates (0, 25, 50, 75, 100, and 200 kg N ha−1) and five residue harvest rates (0, 25, 50, 75, and 100%). We mapped the sustainable limits for each fertilization rate and assessed the effects of fertilization and three key environmental variables – initial SOC, temperature, and precipitation – on sustainable residue harvest rates. We found that, with up to 75 kg N ha−1 fertilization, up to 75% and 50% of crop residue could be sustainably harvested in south‐western and south‐eastern Australia, respectively. Higher fertilization rates achieved little further increase in sustainable residue harvest rates. Sustainable residue harvest rates were principally determined by climate and soil conditions, especially the initial SOC content and temperature. We conclude that environmental conditions and management practices should be considered to guide the harvest of crop residue for bioenergy production and thereby reduce greenhouse gas emissions during the life cycle of bioenergy production.

Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters

Process-based crop models use many cultivar parameters to simulate crop growth. Usually, these parameters cannot be directly measured and need to be calibrated when the crop model is applied to a new environment or a new cultivar. Determining the relative importance of the cultivar parameters to the specific outputs could streamline the calibration of crop models for new cultivars. Sensitivity analysis can quantify the influence of model input parameters on model outputs. We applied the variance-based global sensitivity analysis to the wheat module of the Agricultural Production Systems sIMulator (APSIM) for the first time and calculated the sensitivity of four outputs including yield, biomass, flowering day, and maturity day to ten cultivar parameters including both the main and total effects sensitivity indices. We explored the effects of changing climate, soil, and management practices on parameter sensitivity by analyzing two fertilization rates (0 and 100kgNha−1), across five sites in Australia's cereal-growing regions. Uncertainties for the four outputs with varying cultivar parameters, climate–soil conditions and management practices were evaluated. We found that yield was most sensitive to the cultivar parameters that determine the yield component (grains per gram stem, max grain size, and potential grain filling rate) and the phenology parameters that determine length of the reproductive stages (thermal time from floral initiation to flowing and thermal time from start grain filling to maturity). All ten cultivar parameters affected biomass, amongst which the parameters of vernalization sensitivity and thermal time from floral initiation to flowering were the most influential. Fertilization influenced the rank order of parameter sensitivities more strongly than climate–soil conditions for yield and biomass outputs. Under 0kgNha−1, with the variation of cultivar parameters simulated yield varied from 64 to 3559kgha−1 (minimum and maximum), biomass from 693 to 12,864kgha−1. Fertilization of 100kgNha−1 increased the maximum yield to 9157kgha−1 and biomass to 22,057kgha−1. We conclude that to minimize cultivar-related uncertainty, cultivar parameters should be carefully calibrated when applying the APSIM-wheat model to a new cultivar in a new environment. By targeting the most influential phenological parameters for calibration first and then the yield component parameters, the calibration of APSIM can be streamlined.

Modelling soil carbon and nitrogen dynamics using measurable and conceptual soil organic matter pools in APSIM

Measurable soil organic carbon (SOC) fractions are recommended to be used in soil organic matter (SOM) models to replace the conceptual pools in order to reduce uncertainty related to model initialization. However, changes in SOM pools require derivation of the decomposition rate for the new pools and also impact on simulation of nutrient cycling in soil. Here, we used the Agricultural Production Systems sIMulator (APSIM) to explore the impact of adopting measurable SOM pools on simulated SOC and nitrogen dynamics under three agro-ecosystems at Brigalow, Tarlee and Wagga Wagga in Australia. The decomposition rates for the measurable pools were derived based on the data in the top 30cm soil at Brigalow. With those decomposition rates, the model could predict the variation in observed total SOC, and also captured the dynamics of the measured SOC fractions at the three sites. However, the re-parameterized model overestimated soil nitrogen. Extending the model simulations to centurial scales led to the distinct difference in simulated soil carbon and nitrogen dynamics, due to the difference in pool sizes, decomposition rates and C:N ratios between the two approaches. Sensitivity analysis based on measurable pool approach indicated that simulated soil carbon dynamics was most sensitive to the partitioning of carbon in the sub-pools of new plant material, decomposition rates of particulate and humic organic matter. The simulated soil nitrogen dynamics was most sensitive to the C:N ratio of humic organic matter. In order to accurately simulate the long-term carbon and nitrogen dynamics, more detailed information on the composition of the measurable SOM pools, particularly their C:N ratios and the underlying mechanisms controlling their decomposition and transformation among different pools across different environments are needed.

Effects of fertiliser nitrogen management on nitrate leaching risk from grazed dairy pasture

Abstract Dairy farms are under pressure to increase productivity while reducing environmental impacts. Effective fertiliser management practices are critical to achieve this. We investigated the effects of N fertiliser management on pasture production and modelled N losses, either via direct leaching of fertiliser N, or indirectly through N uptake and subsequent excretion via dairy cow grazing. The Agricultural Production Systems Simulator (APSIM) was first tested with experimental data from fertiliser response experiments conducted on a well-drained soil in the Waikato region of New Zealand. The model was then used in a 20- year simulation to investigate the effect of fertiliser management on pasture response and the impacts on potential leaching losses. The risk of direct leaching from applied fertiliser was generally low, but at an annual rate of 220 kg N/ha exceeded that from urine patches in one out of 10 years. The main effect of N fertiliser on leaching risk was indirect via the urine patch by providing higher pasture yields and N concentrations. Best management practices could include identification of high risk periods based on environmental conditions (e.g. soil moisture, plant growth), avoidance of fertiliser applications in these periods and the use of duration controlled grazing (DCG) to prevent excreta deposition onto the grazing area during critical times. Keywords: Modelling, APSIM, N fertilisation rates, N fertilisation timing, direct and indirect leaching, urine patches

Adaptations for growing wheat in the drying climate of Western Australia

This study investigated the effects of predicted changes in rainfall distribution in marginal (≤325 mm annual rainfall) parts of the south-west Australian wheatbelt and options for management and adaptation of the wheat crop. Field experiments with rain-out shelters and irrigation were conducted in 2008 and 2009 to investigate the interactions of rainfall distribution, row spacing, genotype and timing of nitrogen application on growth, water use and grain yield of spring wheat. Water storage before seeding showed potential to maintain or increase yields despite lower in-season rainfall. Widening row spacing reduced biomass and slowed water use but did not increase grain yield, because of increased soil evaporation and water left in the soil at crop maturity. The Agricultural Production Systems Simulator (APSIM) wheat model was used to investigate the effects of recent and projected climate change on yield in relation to row spacing, phenology and nitrogen. Two climate-change scenarios were applied to historical climatic data to create two plausible future climates (‘optimistic’ and ‘pessimistic’) for the year 2030. None of the strategies tested increased wheat yield under the predicted climate scenarios. Simulated yields at wider row spacings were consistently lower due to insufficient biomass, increased soil evaporation and the inability of the crop to use all of the available water before maturity. Simulated yields of short-season genotypes were always greater than yields of longer season genotypes. Nitrogen regimes had little effect in this study. This study points to several genotypic traits that could improve the performance of wheat grown at wider row spacings. These include early vigour to reduce soil evaporation and increase competition with weeds, greater tillering/biomass to reduce limitation by sink size, and a vigorous root system with appropriate lateral spread and growth to depth to access available soil water.

Deriving pasture growth patterns for Land Use Capability Classes in different regions of New Zealand

Abstract Farm system models are increasingly being used to assess the implications of land use and practice changes on profitability and environmental impacts. Exploring implications beyond individual farms requires the linkage of such models to land resource information, which for pastoral systems includes forage supply. The New Zealand Land Resource Inventory (LRI) and associated Land Use Capability (LUC) database includes estimates of the potential stock carrying capacity across the country, which can be used to derive annual, but not seasonal, patterns of pasture growth. The Agricultural Production Systems Simulator (APSIM) was used, with generic soil profiles based on descriptions of LUC classes, to generate pasture growth curves (PGCs) in three regions of the country. The simulated pasture yields were similar to the estimates in the LRI spatial database, and varied with LUC Class within and across regions. The simulated PGCs also agreed well with measured data. The approach can be used to obtain spatially discrete estimates of seasonal pasture growth patterns across New Zealand, enabling investigation of land use and management changes at regional scales. Key words: APSIM modelling, pasture growth curve (PGC), year to year variability, farm system analysis

Impact of rain-fed cropping on the hydrology and fertility of alluvial clays in the more arid areas of the upper Darling Basin, eastern Australia

The impact of cropping on the hydrology and fertility of Vertosols in the northern Darling Basin (average annual rainfall &gt;550 mm) has received much attention, together with the constraints placed on crop growth by naturally occurring subsoil salt stocks. These factors have not been quantified in the drier (450–550 mm), marginal cropping areas to the west. With widespread adoption of zero tillage technology and the potential for large increases in the capture and storage of rainfall in good seasons, mobilisation of salt could be exacerbated should crop water use be constrained by salt toxicity and/or nutrient deficiency. We investigated the size of salt stocks, historic deep drainage, and nutrient depletion under continuous cropping in the Grey and Brown Vertosols of the Walgett and Coonamble districts of north-western NSW. Soils collected from seven paired sites (cropped v. control native vegetation) showed chloride concentrations &gt;500 mg/kg within 0–1.2 m, high exchangeable sodium percentage (~30%) at depth and deficiency in phosphorus, manganese and zinc. Soil total nitrogen decreased from an average stock of 4.9 t/ha at a rate of 0.008 t/ha.year under cropping within 0–0.1 m and soil carbon stocks decreased from 39 t/ha by 0.20 t/ha.year within 0–0.5 m.. Despite low rainfall, high evaporation and the large water-holding capacity of the cracking clays, there were significant downward shifts in chloride concentrations under cropping. Estimates of deep drainage under continuous cropping using chloride mass balance, chloride-front displacement and crop water-balance modelling with the Agricultural Production Systems Simulator (APSIM) generally agreed (range 0.1–2% of average annual rainfall). Simulations suggested that deep drainage may be increased 5–10-fold under zero-tillage winter cropping due to enhanced capture of rainfall by zero tillage compared with traditional practices. The associated flushing of salt from the root-zone together with correction of nutrient deficiency would enhance crop water use and productivity. Current methods indicate little storage in the subsoil for future deep drainage and that hydraulic conductivity is very low. Hence, the long-term effects of any increase in drainage rates, due to changes in cropping practices and/or climate, on the potential for salinisation of groundwater or transient water logging of the surface, are equivocal.

Sorghum Yield Response to Changing Climatic Conditions in Semi-Arid Central Tanzania: Evaluating Crop Simulation Model Applicability

Decision Support System for Agrotechnology Transfer (DSSAT) and Agricultural Production Systems SIMulator (APSIM) were calibrated and evaluated to simulate sorghum (Sorghum Bicolor L. Moench) var. Tegemeo under current and future climate in central Tanzania. Simulations for both current and future periods were run assuming present technology, current varieties and current agronomy packages to investigate rain-fed sorghum yield response. Simulations by both crop models using downscaled weather data from eight General Circulation Models (GCMs) under the Coupled Model Intercomparison Project phase 5 (CMIP5) and Representative Concentration Pathway (RCP 4.5) by mid-century show a mixture of increase and decrease in median sorghum yields. Four GCMs project yields to increase by 5% - 23.0% and one GCM show a decrease by 2% - 9%. Model simulations under the remaining three GCMs give contrasting results of increase and decrease. Adjustment of crop duration to mimic the choice of growing local cultivars versus improved cultivars seems a feasible option under future climate scenarios. Our simulation results show that current open-pollinated sorghum cultivars would be resilient to projected changes in climate by 2050s but things seem better with long duration cultivars. We conclude that crop simulation models show their applicability as tools for assessing possible impacts of climate change on sorghum due to agreement in the direction of crop yield predictions in five out of eight selected GCMs under projected climate scenarios. The findings provide useful guidance and motivation to government authorities and development agencies dealing with food security issues to prioritize adaptations policies geared to ensuring increased and sustained sorghum productivity in Tanzania and elsewhere.

Potential soil organic carbon stock and its uncertainty under various cropping systems in Australian cropland

The diversity of cropping systems and its variation could lead to great uncertainty in the estimation of soil organic carbon (SOC) stock across time and space. Using the pre-validated Agricultural Production Systems Simulator, we simulated the long-term (1022 years) SOC dynamics in the top 0.3 m of soil at 613 reference sites under 59 representative cropping systems across Australia’s cereal-growing regions. The point simulation results were upscaled to the entire cereal-growing region using a Monte Carlo approach to quantify the spatial pattern of SOC stock and its uncertainty caused by cropping system and environment. The predicted potential SOC stocks at equilibrium state ranged from 10 to 140 t ha–1, with the majority in a range 30–70 t ha–1, averaged across all the representative cropping systems. Cropping system accounted for ~10% of the total variance in predicted SOC stocks. The type of cropping system that determined the carbon input into soil had significant effects on SOC sequestration potential. On average, the potential SOC stock in the top 0.3 m of soil was 30, 50 and 60 t ha–1 under low-, medium- and high-input cropping systems in terms of carbon input, corresponding to –2, 18 and 26 t ha–1 of SOC change. Across the entire region, the Monte Carlo simulations showed that the potential SOC stock was 51 t ha–1, with a 95% confidence interval ranging from 38 to 64 t ha–1 under the identified representative cropping systems. Overall, predicted SOC stock could increase by 0.99 Pg in Australian cropland under the identified representative cropping systems with optimal management. Uncertainty varied depending on cropping system, climate and soil conditions. Detailed information on cropping system and soil and climate characteristics is needed to obtain reliable estimates of potential SOC stock at regional scale, particularly in cooler and/or wetter regions.

Identifying risk-efficient strategies using stochastic frontier analysis and simulation: An application to irrigated cropping in Australia

In irrigated cropping, as with any other industry, profit and risk are inter-dependent. An increase in profit would normally coincide with an increase in risk, and this means that risk can be traded for profit. It is desirable to manage a farm so that it achieves the maximum possible profit for the desired level of risk. This paper identifies risk-efficient cropping strategies that allocate land and water between crop enterprises for a case study of an irrigated farm in Southern Queensland, Australia. This is achieved by applying stochastic frontier analysis to the output of a simulation experiment. The simulation experiment involved changes to the levels of business risk by systematically varying the crop sowing rules in a bioeconomic model of the case study farm. This model utilises the multi-field capability of the process based Agricultural Production System Simulator (APSIM) and is parameterised using data collected from interviews with a collaborating farmer.We found sowing rules that increased the farm area sown to cotton caused the greatest increase in risk-efficiency. Increasing maize area also improved risk-efficiency but to a lesser extent than cotton. Sowing rules that increased the areas sown to wheat reduced the risk-efficiency of the farm business. Sowing rules were identified that had the potential to improve the expected farm profit by ca. $50,000 Annually, without significantly increasing risk. The concept of the shadow price of risk is discussed and an expression is derived from the estimated frontier equation that quantifies the trade-off between profit and risk.