APSIM Simulation Research Articles

Usefulness of techniques to measure and model crop growth and yield at different spatial scales

ContextWithin-field yield variability affects crop production and management decisions. To understand and manage this variability, different techniques have been deployed to measure and monitor the crops (and soils) at various spatial scales, including manual measurements, harvester-mounted yield monitors, proximal and remote sensing and crop simulation modelling. The value of this increasing data availability to enhance process understanding and on-ground management is unclear. ObjectiveThis study aimed to investigate the value of the increasingly available spatial data from different sources to understand important soil-plant processes amenable to improvement in both simulation modelling and for better management decisions for dryland cropping. MethodsWe collected three types of measurement data (manual sampling, sensed data from satellite and drone, and yield maps) over a 10 ha field and conducted simulations using the process-based soil-plant model APSIM at different spatial scales (varied from 1 m2 up to 10 ha). We assessed the agreement between ground measurements and yield maps, analysed the potential to use remotely sensed vegetation indices to estimate yield, and the scale at which process-based modelling could be reliable. ResultsWheat yield extracted from yield map at 1 m2 spatial resolution only explained 30% of the variation in yield measured from 1 m2 manual sampling, with better agreement when data was aggregated to 1 ha strip-scale (R2 = 0.66, NRMSE = 9.1%). Remotely sensed vegetation indices (VI) were better correlated with the yield map when aggregating images to coarse spatial resolution (> 50 m × 50 m), while high-resolution drone VI increased the correlation at finer scales. However, the relationship and the timing of the highest correlation differed between years. APSIM simulated point-based yield measured from manual samples with NRMSE of 19.4%, but it was difficult to capture spatial variation in yield due largely to uncertainties in input data. However, APSIM simulations captured the average crop growth dynamics and yield well at 1 ha strip- and 10 ha whole field scales. ConclusionsThe results highlight the need for caution when using yield maps and remote sensing data to quantify spatial variability and inform spatially explicit management decisions at a fine resolution (e.g., 1 m2). In our case, remote sensing data and yield maps only became consistent and process-based modelling became skilful at scales larger than a 1 ha strip. ImplicationsDespite an increasing amount of high-resolution spatial data, the usefulness at fine resolution needs further investigation, particularly under heterogeneous field conditions. Such data need to be analysed in conjunction with the landscape, soil and climate data to understand the drivers of spatial variability and inform process understanding and modelling. This further implies potential problems in developing spatial management practices at finer scales using such data.

Emulator-based optimization of APSIM-Sugar using the results of sensitivity analysis performed with the software GEM-SA

In most countries where sugarcane is grown, new sugarcane varieties are frequently introduced to the market; however, the existing crop models are not frequently updated. Therefore, experiments are needed to parameterize and optimize crop and cultivar-specific parameters of crop models. In this study, we used GEM-SA -generated output files of a study conducted to perform emulator-based sensitivity analysis of the APSIM-Sugar model using GEM-SA software. We compared the accuracy of simulations performed with optimized parameters using four methods: (1) optimization of 26 parameters of the APSIM-sugar model, (2) optimization of the six most sensitive parameters of the APSIM-sugar model, (3) emulator-based optimization of 26 parameters of the APSIM-sugar model, and (4) emulator-based optimization of the six most sensitive parameters. We also evaluated the computational expensiveness of these optimization methods. The results showed that the emulator-based optimization methods provided fast results compared to optimization using APSIM simulations. Moreover, the emulator-based optimization of the six selected most sensitive parameters (which took only a few minutes) provided almost similar simulation results to those obtained with all optimized parameters using APSIM optimization, which took days. Considering the accuracy and computational complexity, we propose to perform the optimization of the most sensitive parameters using an emulator-based approach. Since the sensitivity analysis results were used in this study, this optimization process could be directly coupled with the emulator-based sensitivity analysis explained by Gunarathna et al. (2019c).

Calibrating APSIM for forage sorghum using remote sensing and field data under sub-optimal growth conditions

CONTEXTMechanistic sorghum models have been mostly used to estimate sorghum yield for grain sorghum for a range of genotype, management, and environmental conditions. There is a lack of model testing for crop growth and development responses for forage genotypes and information for phenological parameterization under sub-optimal water and nitrogen stress conditions in forage systems. OBJETIVEThe aims of this study were to (i) use NDVI to parametrize APSIM model to estimate forage sorghum phenology, (ii) calibrate APSIM to simulate green cover, intercepted solar radiation and aboveground biomass, and (iii) quantify the variance of inter-annual aboveground biomass and the effect of water availability on forage sorghum biomass under sub-optimal environment × management combinations. METHODSWe used climate, soil, management records and sorghum crop observations collected from farm and field experiments in Argentina and Australia. NDVI values were gathered from Sentinel-2 and a handheld optical sensor and then related to fAPAR measurements. Phenological stages were derived from fAPAR seasonal dynamics and implemented as input in the APSIM calibration. Finally, we assessed the temporal AGB variability through long-term simulations analysis. RESULTS AND CONCLUSIONSNDVI seasonal dynamics accurately represented the fraction of the absorbed photosynthetically active radiation (R2=0.92) and then, the remote-sensing parametrized APSIM model satisfactorily simulated crop phenology (CCC=0.75-0.92, NRMSE=9-22%). The model was also able to satisfactorily simulate crop growth (CCC=0.89 and NRMSE=24.8% for green cover; CCC=0.81 and NRMSE=34.6% for intercepted solar radiation; CCC=0.91 and NRMSE=37% for aboveground biomass). APSIM simulations during 22 years across 5 contrasting locations showed high inter-annual variability of aboveground biomass (CV=47%), mainly driven by inter-annual variation of soil water availability (CV=20%). Our study demonstrated that (i) remote sensing data was a reliable source for APSIM phenology parametrization, (ii) the model was able to satisfactorily simulate crop growth and development of forage sorghum under sub-optimal conditions across several genotype × environment × management combinations and (iii) water availability was the main driver of aboveground biomass inter-annual variance. SIGNIFICANCEGiven the pressure of the global human population to satisfy an increasing demand for food, our results provide a new path for the combined use of remote sensing and mechanistic modelling to improve forage sorghum biomass estimations in marginal environments.

Planting Period Effects on Wheat Productivity and Water Footprints: Insights through Adaptive Trials and APSIM Simulations

Scarcity of fresh water and climate change are the two main threats for wheat production in South Asia. Tweaking wheat planting period could be an effective cost-smart strategy to mitigate these stresses. To evaluate the performance of three leading wheat varieties under different planting periods in pragmatic on-farm environments, trials were carried out during 2019–2020 and 2020–2021. Further, to have greater insights on long-term temporal scale, 22 years (2000 to 2021) of crop simulation data were analyzed to identify the optimum planting period of wheat for higher yield and water productivity using the APSIM cropping systems simulation model. The result showed that first fortnight of November (PD1)-sown crop resulted in higher grain yield and more irrigation water use efficiency. Wheat sown during PD1 and in the second fortnight of November (PD2) had 20–25% lower blue water requirement than the second fortnight of December (PD4) crop in the long run. To produce one tonne of wheat grain required an additional 20, 60 and 83 m3 irrigation water when the crop was sown at PD1, PD2, PD3 (first fortnight of December) and PD4, respectively. It was observed that PD4 reduced wheat yields by 20–22% compared to sowing on PD1 and PD2 and every 15 days’ delay in wheat planting after 15 November reduced the length of the crop growing season by 4–5 days. Hence, the early wheat planting is proven superior in harnessing maximum yield with minimum burden on blue water resources.

Assessing errors during simulation configuration in crop models – A global case study using APSIM-Potato

Crop models are usually developed using a test set of data and simulations representing a range of environment, soil, management and genotype combinations. Previous studies demonstrated that errors in the configuration of test simulations and aggregation of observed data sets are common and can cause major problems for model development. However, the extent and effect of such errors using Agricultural Production system SIMulator Next Generation (APSIM) crop models are not usually considered as a source of model uncertainty. This is a methodological paper describing several approaches for testing the APSIM simulation configuration to detect anomalies in the input and observed data. In this study, we assess the simulation configuration process through (i) quality control analysis based on a standardised climate dataset (ii) outlier identification and (iii) a palette of visualization tools. A crop model – APSIM-Potato is described to demonstrate the main sources of error during the simulation configuration and data collation processes. Input data from 426 experiments conducted from 1970 to 2019 in 19 countries were collected and configured to run a model simulation. Plots were made comparing simulation configuration data and observed data across the entire test set so these values could be checked relative to others in the test set and with independent datasets. Errors were found in all steps of the simulation configuration process (climate, soil, crop management and observed data). We identified a surprising number of errors and inappropriate assumptions that had been made which could influence model predictions. The approach presented here moved the bulk of the effort from fitting model processes to setting up broad simulation configuration testing and detailed interrogation to identify current gaps for further model development.

Controlled traffic farming delivers improved agronomic performance of wheat as a result of enhanced rainfall and fertiliser nitrogen use efficiency

ABSTRACT This study investigated the agronomic response and economic return of wheat grown in compacted and non-compacted soils to represent the conditions of non-controlled (non-CTF) and controlled traffic (CTF) systems, respectively. Yield-to-nitrogen responses were derived after application of urea, DMPP-treated urea, and UAN at rates between 0 and 300 kg ha−1 N. Soil properties were measured to guide parametrisation of APSIM, which was used to assess long-term (50 years) effects of CTF and non-CTF soil conditions on crop productivity, rainfall-use efficiency (RUE) and surface runoff. Grain yield and yield components were significantly higher in CTF compared with non-CTF. When N inputs were optimised, N use efficiency (NUE) was more than double in CTF (≈23%) compared with non-CTF (≈9%). RUE was about 15% higher in CTF, which concurrently reduced the amount of surface runoff compared with non-CTF. For years with average rainfall (240-mm in-crop), yield penalties of up 12% may be expected in non-CTF. APSIM simulations showed that increased productivity, and inter-annual yield stability, can increase gross margin of wheat by AUD30-50 ha−1 depending on in-crop rainfall and the tillage method used. In non-CTF systems, improvements in NUE and RUE are constrained by soil compaction. Enhanced efficiency fertilizers cannot compensate for other stresses caused by compaction and therefore cannot achieve the same NUE and RUE as the CTF system. Adoption of CTF in water-constrained environments improves profitability and resource-use efficiency.

Quantifying the value proposition for white clover persistence on a New Zealand summer-dry hill-country farm

The improvement in forage quality and quantity of summer-dry hill country pasture resulting from the introduction of clover is well recognised. However, ensuring the persistence of the commonly availablecultivars is challenging, in the face of seasonal moisture stress, intensive grazing, competition from established well-adapted pasture species, low soil fertility and low soil pH – conditions typical of the East Coast of the North Island. Here we quantify the value proposition associated with the introduction of white clover into a case study on a Gisborne sheep and beef farm, using a six-step process. A topographically explicit approach is taken, using an understanding of the underlying spatial variability, based on a combination of soil and pasture measurements, APSIM simulation modelling of pasture growth and farm system modelling of enterprise performance. We show that from a baseline of a typical low-fertility, diverse species hill country pasture, white clover introduction can increase spring and summer forage consumption by 17%, enabling inclusion of an additional 6-month bull finishing enterprise generating a 32% greater carcass weight production and leading to a 49% improvement in farm system EBIT. This represents a positive net present value of over $360,000 for the original investment in white clover establishment into existing pastures.

Soil carbon sequestration potential in a Vertisol in central India- results from a 43-year long-term experiment and APSIM modeling

Soil organic matter dynamics in terrestrial ecosystems are controlled by complex interactions between various factors such as climate, soil, and agricultural management practices. We utilized a process-based crop model, APSIM, to simulate long-term soil organic carbon (SOC) dynamics for a soybean-wheat cropping system under nitrogen (N) and farmyard manure management (FYM) practices for a 43-year old experimental dataset in India. The APSIM was parameterized and validated to predict grain yield and SOC stock. The validated model was then used to evaluate the impacts of different management practices on SOC dynamics in the top 30 cm of soil through scenario modeling. The results of the APSIM simulations demonstrated that improved N and FYM management practices had great potential to increase SOC sequestration in these Vertisols. The equilibrium SOC concentration under different N management practices increased with a higher N application rate, with the integrated application of N with FYM showing the maximum rate. The optimum N (Nopt) rate for maximum SOC sequestration was estimated to be 155 kg ha−1 for wheat in the studied Vertisol and the time to reach steady-state of the site was 104 years. The Nopt increased SOC by about 28.6% over the initial concentration. We found that the APSIM was robust in predicting long-term changes in SOC stock (Index of agreement = 0.79 and root mean square error = 3.33 Mg ha−1, R2 = 0.92, mean bias error = −1.08) for a Vertisol soil of central India, in this case under a soybean-wheat cropping system. The study results highlighted that balanced fertilization is the key to sustaining SOC stock in the long-term for Vertisols.

Maize production and environmental costs: Resource evaluation and strategic land use planning for food security in northern Ghana by means of coupled emergy and data envelopment analysis

This paper applies an integrated methodology which is constituted of the following: (i) the Emergy-Data Envelopment Analysis (EM-DEA), (ii) environmental Cost-Benefit Analysis (CBA), (iii) Value Chain Analysis (VCA), and (iv) Sustainability Balanced Scorecard (SBSC) approaches, -to support multicriteria decision analysis (MCDA) for strategic agricultural land use planning, which could contribute to improve food security in northern Ghana. Five scenarios of land use and resource management practices for maize production were modelled. The business-as-usual scenario was based on primary data, which were collected using semi-structured questionnaires administered to 56 small-scale maize farmers through personal interviews. The dominant land use was characterised by an external input ≤12 kg/ha/yr inorganic fertilizer with/without the addition of manure in rainfed maize systems. The project scenarios were based on APSIM simulations of maize yield response to 0, 20, 50 and 100 kg/ha/yr urea dosages, with/without supplemental irrigation. The scenarios were dubbed as follows: (1) no/low input systems were denoted by Extensive0, Extensive12, and Intercrop20, and (2) moderate/high input systems were denoted by Intensive50, and Intensive100. The EM-DEA approach was used to assess the resource use efficiency (RUE) and sustainability in maize production systems, Ghana. The measured RUE and sustainability were used as a proxy for further analyses by applying the environmental CBA and VCA approaches to calculate: (a) the environmental costs of producing maize, i.e. resource use measured as total emergy (U), and (b) benefits from the yielded maize, i.e. (b i) food provision from grain measured in kcal/yr, and (b ii) potential electricity (bioenergy) which could be generated from residue measured in MWh/yr. The information which was derived from the applications of the EM-DEA, CBA and VCA approaches was aggregated by applying the SBSC approach to do a sustainability appraisal of the scenarios. The results show that, when labour and services are included in the assessment of RUE and sustainability, Intercrop20 and Intensive50 achieved greater marginal yield, better RUE, sustainability and appraisal score. The same scenarios caused lesser impacts in terms of expansion of area cultivated compared to Extensive0 and Extensive12. Meanwhile the impacts of Intercrop20 and Intensive50 in terms of ecotoxicity, emissions, and demand for resources (energy, materials, labour and services) were lesser compared to Intensive100. The implications of the various scenarios are discussed. The environmental performance of the scenarios are compared to maize production systems in other developing regions in order to put this study within a broader context. We conclude that, the EM-DEA approach is useful for assessing RUE and sustainability of agricultural production systems at farm and regional scales, as well as in connecting the management planning level and regional development considerations.

Lateral spread affects nitrogen leaching from urine patches

Nitrate leaching from urine deposited by grazing animals is a critical constraint for sustainable dairy farming in New Zealand. While considerable progress has been made to understand the fate of nitrogen (N) under urine patches, little consideration has been given to the spread of urinary N beyond the wetted area. In this study, we modelled the lateral spread of nitrogen from the wetted area of a urine patch to the soil outside the patch using a combination of two process-based models (HYDRUS and APSIM). The simulations provided insights on the extent and temporal pattern for the redistribution of N in the soil following a urine deposition and enabled investigating the effect of lateral spread of urinary N on plant growth and N leaching. The APSIM simulation, using an implementation of a dispersion-diffusion function, was tested against experimental data from a field experiment conducted in spring on a well-drained soil. Depending on the geometry considered for the dispersion-diffusion function (plate or cylindrical) the area-averaged N leaching decreased by 8 and 37% compared with simulations without lateral N spread; this was due to additional N uptake from pasture on the edge area. A sensitivity analysis showed that area-averaged pasture growth was not greatly affected by the value of the dispersion factor used in the model, whereas N leaching was very sensitive. Thus, the need to account for the edge effect may depend on the objective of the simulations. The modelling results also showed that considering lateral spread of urinary N was sufficient to describe the experimental data, but plant root uptake across urine patch zones may still be relevant in other conditions. Although further work is needed for improving accuracy, the simulated and experimental results demonstrate that accounting for the edge effect is important for determining N leaching from urine-affected areas.

Exploring adaptations of groundnut cropping to prevailing climate variability and extremes in Limpopo Province, South Africa

Adapting groundnut production to the high climatic variability and extremes prevailing in Limpopo (South Africa) requires season- and site-specific exploitation of genotype x environment x management interactions. Notably, the El Niño-Southern Oscillation phenomenon (ENSO) causes distinctive seasonal rainfall anomalies in the province: Weather records over the period 1955–2010, showed El Niño years with an average of 296 mm growing season rainfall, neutral years with 349 mm and La Niña with 434 mm. We hypothesized that (i) early planting date is crucial for ensuring high productivity of groundnut and (ii) early maturing cultivars can ensure satisfactory production even in El Niño years, both options aiming at escaping prolonged periods of drought. To evaluate our hypotheses, we conducted a three year on-station trial with three planting dates and two cultivars, a one year trial at two sites comparing the two cultivars, tested crop model APSIM against field trial data, and, finally, performed long-term gridded crop model simulation runs for all cultivated land (i.e. 14,739 grid cells at a resolution 0.01°) in Limpopo exploring the effect of the cultivars and planting dates on groundnut yield. Field trials showed that early planting was superior to later plantings, irrespective of the weather year. Even in the very dry El Niño year 2016, yields of more than 1000 kg/ha were achieved with early planting, while planting later was not possible due to severe soil water deficits. Cultivar ICGV 03796 performed slightly better than ICGV 99257 due to faster ground cover closure, but no difference in phenology was observed. After evaluation of the model against the field trial data was found satisfactory, APSIM simulations showed that highest yields were achieved in La Niña years (median 996 kg/ha across all grids) and smallest in El Niño years (654 kg/ha across all grids). However, as in the field trials, we observed on average over all grid cells higher grain yields for early planting for all weather years as effective rainfall captured was higher than for later plantings. Longer growth duration by cultivar ICGV 03796 appeared to be more productive than an earlier maturing cultivar that we added into the simulation analysis as a third cultivar. However, a closer look at the spatial pattern of the simulation analysis revealed that for the generally drier regions of Limpopo, this cultivar with shorter growth duration would indeed be more productive in El Niño years. It showed that such season-specific adaptation would increase yields only by 2.9% for Limpopo province in total, but yield gains of more than 10% could be obtained for 11.3% of the grid cells in El Niño years. Hence, this study demonstrated that management recommendations at provincial scale explicitly need to consider in more detail a site- and season-specific approach.

Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments.

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development of genotypes with optimized VPD-limited transpiration traits and deployment of these crops in water limited growing environments. The energy sorghum model and VPD-limited transpiration trait implementation are made available to simulate performance in other target environments.

A standardized workflow to utilise a grid-computing system through advanced message queuing protocols

Point-based crop models are frequently used to investigate the interaction of genotype, environment and management (G × E × M) for breeding programs and adaptation research. These studies require the processing of millions of simulations to make assessments in national scales. Here we demonstrate a platform to run crop simulations with HTCondor as implemented across more than 12 000 cores on CSIRO network. The workflow of HTCondor usages was built through a server-client structure with standardized messages (simulations), which were queued by message queue sever. The new workflow maximized the performance of the CSIRO HTCondor service and could simultaneously utilize more than 8000 cores. A case study of 4.8 million APSIM simulations (ca. 2 mins each on model desktop), was completed in 32.5 h. This platform makes it is possible to assess all combinations of G × E × M in a short period. The generic platform has since been used to run other massive serial processing tasks.

Impact of climate changes on existing crop-livestock farming systems

The state of Western Australia is a major producer and exporter of crops and livestock. Mixed farming systems are typical agricultural enterprises in the Western Australian wheatbelt where climate drives the productivity and profitability of these farms and therefore the effects of likely climate change on their performance need to be understood. Here the effects of climate change projected at 2030 were evaluated compared to a baseline period (1980–1999) on mixed farming systems at paddock, enterprise and whole farm scales using the coupled APSIM and GRAZPLAN biophysical simulation models. The yield of different crops, livestock production and gross margins were assessed under current and projected climates using current farming technology and management practices. Representative mixed-farm systems were selected along a climate transect. Modelling analysis suggests that current production levels and gross margins of mixed farm systems in Western Australia will not be sustained in 2030 climate conditions except in areas of moderately high-rainfall. Whole farm gross margin declined at all site×potential climate scenarios between 1% and 22% except in moderately high rainfall where gross margin increased by up to 4% under a ‘hot and moderate change in rainfall’ climate. Projected crop yields declined for most of the crop×site×potential climate combinations, with greatest declines under a hot and dry climate (at driest margin of transect) in which wheat, barley, canola, and lupin yield declined up to 16%, 15%, 21%, and 27%, respectively. Increase in yield was predicted for wheat and barley at some of the site×potential climate s. Wheat yield increased only under moderately high rainfall region by 6% while barley increased by 1%. Simulated cropping gross margin was also shown to decline by between >1% and 23%, except for the moderately high-rainfall site where cropping gross margins were projected to increase by up to 3%. Changes in simulated livestock production were smaller and less variable than for crop production. The change in weight of livestock sold across sites×potential climate combinations ranged between −3% and +3%. Livestock gross margin varied between −11% and +6%. Modelling results indicated a greater fertilisation effect of the elevated CO2 on pasture production than on crop yield and biomass particularly in drier sites. But however, this could not offset negative impact of climate change under hot potential climates. The main negative environmental impacts from the projected climate change were declines in annual net primary production (ANPP), ground cover and water use efficiency mostly at drier sites. Whole farm N2O emission declined significantly for the majority of site×potential climate combinations, while smaller decreases in ruminant CH4 emission were predicted. In 2030, returns from livestock enterprises are predicted to be smaller, but less variable than from cropping and with increasing probability of success in drier regions. Reduced variability in financial return is important from the perspective of whole farm risk management. Shifts in enterprise mix in dryland mixed-farming systems towards increased livestock may be a helpful strategy in adapting to climate change and managing the associated financial risks.

Analysis of options for increasing wheat (Triticum aestivum L.) yield in south-eastern Australia: The role of irrigation, cultivar choice and time of sowing

Yield of a dryland crop such as wheat is affected by environmental, management and genotypic factors. Field experiments and computer simulations were conducted to understand the effect of supplemental irrigation, type of cultivar and time of sowing (using simulation) on grain yield and quality of two wheat varieties EGA Gregory and Livingston. There was significant effect of variety and watering regime on grain yield, harvest index, kernel weight and number and protein content. The maximum grain yield of 6.10tha−1 was obtained from the irrigated mid-late flowering variety EGA Gregory in the year the crop was sown early. The effect of irrigation on the late flowering variety EGA Gregory was higher than that on the early flowering variety Livingston. The grain water use efficiency of EGA Gregory was higher than that of Livingston in the year it was sown early. APSIM simulations of yield using historical weather data of the past 114 years show that as the sowing date is delayed, the decrease in yield of EGA Gregory becomes higher than that of Livingston and by mid-June sowing, the grain yield of Livingston becomes higher than that of EGA Gregory. As the sowing is delayed, the differences between the anthesis dates, and also grain yields of the two varieties get smaller. The simulation results show that the grain yield of late-sown EGA Gregory and Livingston increases with the amount of in-crop rainfall. However, when sown early, the yield of Livingston decreases with the amount of in-crop rainfall. Farmers need to have different cultivars on hand and sow according to seasonal condition. An early-sown mid-lateflowering cultivar is less risky.

Impacts of drought on grape yields in Western Cape, South Africa

Droughts remain a threat to grape yields in South Africa. Previous studies on the impacts of climate on grape yield in the country have focussed on the impact of rainfall and temperature separately; meanwhile, grape yields are affected by drought, which is a combination of rainfall and temperature influences. The present study investigates the impacts of drought on grape yields in the Western Cape (South Africa) at district and farm scales. The study used a new drought index that is based on simple water balance (Standardized Precipitation Evapotranspiration Index; hereafter, SPEI) to identify drought events and used a correlation analysis to identify the relationship between drought and grape yields. A crop simulation model (Agricultural Production Systems sIMulator, APSIM) was applied at the farm scale to investigate the role of irrigation in mitigating the impacts of drought on grape yield. The model gives a realistic simulation of grape yields. The Western Cape has experienced a series of severe droughts in the past few decades. The severe droughts occurred when a decrease in rainfall occurred simultaneously with an increase in temperature. El Nino Southern Oscillation (ENSO) appears to be an important driver of drought severity in the Western Cape, because most of the severe droughts occurred in El Nino years. At the district scale, the correlation between drought index and grape yield is weak (r≈−0.5), but at the farm scale, it is strong (r≈−0.9). This suggests that many farmers are able to mitigate the impacts of drought on grape yields through irrigation management. At the farm scale, where the impact of drought on grape yields is high, poor yield years coincide with moderate or severe drought periods. The APSIM simulation, which gives a realistic simulation of grape yields at the farm scale, suggests that grape yields become more sensitive to spring and summer droughts in the absence of irrigation. Results of this study may guide decision-making on how to reduce the impacts of drought on food security in South Africa.

Efficient Nitrogen and Water Management for the Soybean–Wheat System of Madhya Pradesh, Central India, Assessed Using APSIM Model

A series of long-term simulations were carried out to investigate alternative management practices to increase grain yields of soybean and wheat by optimizing sowing dates, nitrogen (N) and water requirements, along with complimenting farmyard manure (FYM) as a N source in the soybean–wheat cropping system of Madhya Pradesh. The APSIM simulation study showed that the mean soybean yield ranged from 1.0 to 1.6 t ha−1 for the different dates of sowing. The average wheat grain yield was 3.2–3.9 t ha−1, whereas, the crop sown on 15 November gave the highest yield. In this region, there is a potential to increase soybean and wheat yields by 0.6 and 2.2 t ha−1, respectively. Among the various irrigation practices simulated, five irrigations of 60 mm at 20 days interval was the best option for wheat. Application of 16 t FYM ha−1 to soybean produced 50 % higher wheat yield than the same amount of FYM applied to wheat. The wheat yield obtained from inorganic application of N was at par with that obtained from the application of integrated and organic sources. However, the amount of N loss from the integrated use of fertilizer N was lower than that from the current recommended practice for the region. Application of FYM alone or in combination with inorganic fertilizer maintained higher soil organic carbon concentration as compared to the application of inorganic fertilizer alone. Thus, the model provided a mean of evaluating alternative crop N and water management options for effectively managing the soybean–wheat cropping system.

Quantifying potential yield and lodging-related yield gaps for irrigated spring wheat in sub-tropical Australia

Approximately 15% of global wheat production (40% of the wheat produced by developing countries) occurs in irrigated, low-rainfall regions of the subtropics. Irrigated wheat is uncommon in sub-tropical Australia because cotton is more profitable, but high grain prices in 2008 triggered a record area of irrigated wheat production. Unfortunately, widespread lodging occurred and yields were lower than expected, although the losses directly attributable to lodging were unknown.Crop models have not been previously validated for calculating potential yield and yield gaps of mechanised, lodging-susceptible production systems. This study aimed to determine potential yield, farmer-realised yield, and water use requirements of lodging-prone irrigated wheat in sub-tropical Australia, using field monitoring and the APSIM simulation model. Yield data were obtained in 2008 and 2009 from 16 commercial irrigated wheat fields and multiple treatments from four irrigated experiments, and used to assess APSIM's ability to predict grain yield of irrigated wheat.APSIM predicted biomass production satisfactorily in 2008 but substantially over-predicted grain yield of lodged fields. The mean difference (yield gap) between APSIM-estimated potential yield and farmer-realised yield was 1.0tha−1 in non-lodged fields, and 2.7tha−1 in lodged fields. The average effect of lodging was therefore a decrease of 1.7tha−1; the difference between the yield gap calculated for lodged and non-lodged fields. Yield gaps in lodged fields were highly correlated with soil N content at sowing, with most of the lodged fields having more than 300kgha−1 of soil N at sowing to a depth of 90cm.In 2009, management of commercial fields aimed to avoid lodging by using the canopy management technique of in-season N application. APSIM generally under-predicted biomass production and yield in these fields, suggesting that the N uptake parameters in APSIM may require adjustment. Observed yields from fields of a quick-maturing cultivar that experienced little lodging were simulated accurately when N was assumed to be non-limiting.The simulation study found that the potential yield of irrigated spring wheat in sub-tropical Australia is currently between 8 and 9tha−1, and average growing season evapotranspiration of such crops was approximately 500–550mm. Potential yield of furrow-irrigated fields with one or two-metre wide beds were 16% and 8% lower, due to the area occupied by the 60cm wide unsown furrows between irrigation beds.This study demonstrated the value of using validated simulation models for estimating potential yield and yield gap analysis. Yield gap analysis also enabled the identification of agronomic characteristics (high levels of soil residual N) which probably contributed to lodging. Further investigation is required to determine whether alternative varieties or the canopy management technique of in-season N application can increase the potential yield of irrigated spring wheat above the levels reported here.

Calibration and validation of models for short-term decomposition and N mineralization of plant residues in the tropics

Insight of nutrient release patterns associated with the decomposition of plant residues is important for their effective use as a green manure in food production systems. Thus, this study aimed to evaluate the ability of the Century, APSIM and NDICEA simulation models for predicting the decomposition and N mineralization of crop residues in the tropical Atlantic forest biome, Brazil. The simulation models were calibrated based on actual decomposition and N mineralization rates of three types of crop residues with different chemical and biochemical composition. The models were also validated for different pedo-climatic conditions and crop residues conditions. In general, the accuracy of decomposition and N mineralization improved after calibration. Overall RMSE values for the decomposition and N mineralization of the crop materials varied from 7.4 to 64.6% before models calibration compared to 3.7 to 16.3 % after calibration. Therefore, adequate calibration of the models is indispensable for use them under humid tropical conditions. The NDICEA model generally outperformed the other models. However, the decomposition and N mineralization was not very accurate during the first 30 days of incubation, especially for easily decomposable crop residues. An additional model variable may be required to capture initial microbiological growth as affected by the moisture dynamics of the residues, as is the case in surface residues decomposition models.

Simulating soybean–wheat cropping system: APSIM model parameterization and validation

In recent years, nutrient management of soybean–wheat systems in central India has become a cause for concern because of stagnation of grain yields of soybean and wheat. The reduction in grain yield was mainly attributed to a suboptimal supply of nutrients to both the crops, use of poor quality farmyard manures and erratic distribution of rainfall during soybean's growing season and unavailability of irrigation water during wheat season. In this connection, the crop growth simulation models are handy in identifying the constraints to yield and recommending appropriate management practices to optimize the productivity of soybean–wheat system. To achieve this, the APSIM model was parameterized and validated for soybean and wheat crop of subtropical central India.Independent data set was used to parameterize soybean cultivar (JS 335) and wheat cultivar (Sujata) to be used for APSIM simulation. Genetic coefficient generated from this study was used for subsequent model validation. The data on water use, N uptake, grain yield and soil organic C from an ongoing long-term experiment was used for validation purpose. Three nutrient treatments, viz., control (no nutrient), inorganic (recommended rate) and FYM (8tha−1 to soybean and 16tha−1 to wheat) were used to validate the APSIM model. For organic treatments, we simulated N management using the FYM as the source of plant available N under field condition. The model was parameterized by specifying the N mineralized from the manure in the laboratory incubation. The model predicted successfully grain yield and N uptake under FYM treatments in soybean and wheat. For other treatments, model prediction was satisfactory in most of the cases in simulating water and organic carbon, grain yield and N uptake by both the crops. The discrepancy observed between the observed and predicted yield in the control under soybean was due to the P limiting condition of the treatment rather than the model. The predicted variability of crop yield was also due to the variation of weather during soybean growing season and amount of irrigation and N used during wheat growing season. Therefore, this APSIM simulation study can satisfactorily be used to make appropriate management decisions to provide farmers and others with alternative options for nutrient management for soybean–wheat cropping systems.