Agricultural Production Systems Simulator Research Articles

Impact of Future Climate Change on Wheat Production: A Simulated Case for China’s Wheat System

With regard to global climate change due to increasing concentration in greenhouse gases, particularly carbon dioxide (CO2), it is important to examine its potential impact on crop development and production. We used statistically-downscaled climate data from 28 Global Climate Models (GCMs) and the Agricultural Production Systems sIMulator (APSIM)–Wheat model to simulate the impact of future climate change on wheat production. Two future scenarios (RCP4.5 and RCP8.5) were used for atmospheric greenhouse gas concentrations during two different future periods (2031–2060 referred to as 40S and 2071–2100 referred to as 80S). Relative to the baseline period (1981–2010), the trends in mean daily temperature and radiation significantly increased across all stations under the future scenarios. Furthermore, the trends in precipitation increased under future climate scenarios. Due to climate change, the trend in wheat phenology significantly advanced. The early flowering and maturity dates shortened both the vegetative growth stage (VGP) and the whole growth period (WGP). As the advance in the days of maturity was more than that in flowering, the length of the reproductive growth stage (RGP) of spring wheat was shortened. However, as the advance in the date of maturity was less than that of flowering, the RGP of winter wheat was extended. When the increase in CO2 concentration under future climate scenarios was not considered, the trend in change in wheat production for the baseline declined. In contrast, under increased CO2 concentration, the trend in wheat yield increased for most of the stations (except for Nangong station) under future climatic conditions. Winter wheat and spring wheat evapotranspiration (ET) decreased across all stations under the two future climate scenarios. As wheat yield increased with decreasing water consumption (as ET) under the future climatic conditions, water use efficiency (WUE) significantly improved in the future period.

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate.

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.

Simulating effect of climate change on finger millet (Eleusine Coracana) yield under selected tillage practices and soil fertilizers inputs in semi-arid lower Eastern Kenya

The current study, conducted in semi-arid Machakos and Kitui Counties of Kenya, simulated effect of climate change (CC) on finger millet yield under different soil fertilizer inputs (SFI), tillage practices (TP) and projected CC scenarios using the Agricultural Production Systems Simulator (APSIM) model. A randomized complete block design with split plot arrangement was employed. Main plots were TP; oxen plough-OP, ridges and furrows-RF with SFI; Farm yard manure-FYM, Triple super phosphate-TSP + Calcium Ammonium Nitrate-CAN (TSP+CAN) and no fertilizer input as split-plots. The CC scenarios considered were; Current Rainfall (R0) and Temperature (T0) provided the baseline, R1 (R0+10% increase in rainfall), R2 (R0-10% decrease in rainfall), T1 (T0 + 20C) and T2 (the combined effects of 10% decrease in rainfall and 20C increase in temperature (-10%+20C). Significantly (P≤0.001) higher finger millet yields were obtained in TSP+CAN treated plots compared to FYM and control in both Kitui (with higher yields) and Machakos. Comparatively, finger millet yields were well simulated with moderate RMSE (1.04, 0.94) values in OP and RF in Kitui and low values (0.18) in OP in Machakos). Simulated finger millet yields mirrored measured yields, and were higher in Kitui (RF) than Machakos (OP) with TSP+CAN recording highest simulated yields compared to FYM and control. R1 (R0+10% rainfall) registered significantly high finger millet yields under OP and RF with application of TSP+CAN in both sites. The lowest finger millet yields, across sites were noted in T2 (-10% rainfall+20C), in decreasing order; TSP+CAN; FYM and control under OP and RF. Finger millet yields measured and simulated, insitu and across CC scenarios, indicated that application of TSP+CAN under conservation tillage practices (RF in Kitui and OP in Machakos) consistently gave superior yields compared to FYM and control. In the event of change of climate favouring increased rainfall (R1), finger millet grown under RF and OP with application of TSP+CAN may have the potential to adapt to climate change and enhance food and nutritional security. Further studies, mainly focusing on moisture conservation and breeding of drought tolerant crops, are nonetheless recommended to generate possible CC adaptation strategies under R2, T1 and T2 possible climate change scenarios in semi-arid regions of Kenya.

Crop model improvement in APSIM: Using wheat as a case study

Abstract The process of building and improving a broadly useful set of crop models is a major undertaking and the APSIM (Agricultural Production system SIMulator) development community conduct this work separately from projects that have specific uses of the models. This paper presents a set of standards that a modern crop model should meet, and describes the approaches and software the APSIM development community are using to build and maintain models that meet these standards. The latest version of APSIM combines a range of tools in a single user interface (UI) to assist model developers. It uses a modern version control system to ensure model reliability and a modern distribution system to ensure users can easily access models and receive updates. The wheat model is used as an example to describe the development process using the APSIM platform. Firstly, a test set for the model was developed: • Forty-eight experiments, giving 655 treatments, were collated into a database. • Each of these treatments was configured as a test simulation using the Experiment component in the UI to hasten the process and reduce human error. • The model was implemented in the UI using the plant modelling framework for a visual and flexible approach to model construction. • Graphs and statistics for assessing model performance were constructed in the UI. • From the UI, changes were made to the model, and its performance was reviewed each time until developers were happy with it. • Memo fields were added into the UI to describe experiments and capture the rationale and citations for model structure and parameterisation. This system facilitated a number of science improvements to the wheat model, including new canopy and phenology models and the compilation and more thorough analysis of a large test dataset. The test set was submitted to APSIM governance for review and approval for general release. Once approved, the model was included in the APSIM distribution as a read-only file so users cannot change it. The test set was put into version control to provide a baseline for assessing model performance. Documentation was automatically generated from the test set, providing a full and up-to-date technical description of the model and its evaluation. This demonstrates a robust yet flexible and efficient approach for improving and distributing crop models, facilitated by purpose-designed software.

APSIM Next Generation: Overcoming challenges in modernising a farming systems model

From 1990, the Agricultural Production Systems sIMulator (APSIM) has grown from a field-focused farming systems framework used by a small number of people, into a large collection of models used by thousands of modellers internationally. The software grew to consist of several hundred thousand lines of code in multiple programming languages. This has led to a large, complex software ecosystem that is difficult to maintain. In addition, systems modellers increasingly require software systems that integrate multiple disciplines, can represent evermore complex farming systems, can run on multiple operating systems (desktop, web, mobile), can operate at or be adjusted to multiple temporal and spatial scales (field, farm, region, continent, global) and run faster for larger simulation analyses. This is difficult to achieve in an aging framework.For these reasons, the APSIM Initiative is building the next generation of APSIM. This manuscript outlines the approach taken and lessons learnt.

Performance of a fertiliser management algorithm to balance yield and nitrogen losses in dairy systems

To demonstrate the use of a previously developed fertilisation algorithm and to determine its potential effects on nitrogen (N) losses from grazed pastoral systems, a simulation study was performed using the Agricultural Production Systems Simulator (APSIM). The study considered a dairy system with irrigated ryegrass pasture on a silt loam soil in the Canterbury region of New Zealand. Firstly, the algorithm was parameterised for each month based on pasture yield and N contents from simulation run over 20 years using a wide range of N fertilisation rates. The algorithm was then used in the simulation of fertilisation management of a hypothetical dairy farm under different scenarios where its performance for increasing pasture yield with more efficient N use was tested. The scenarios were based on different yield targets for the proposed algorithm (50, 75, 90 or 100% of the average maximum yield) and included scheduled fertilisation to mimic more typical management. For more realistic evaluation, the simulations took into account changes in stocking rates and N flows in the farm resulting from the different fertiliser management. The simulations also considered the uneven return of urinary N by grazing animals, which are crucial to determine N losses in these systems.Both pasture yield and N losses were in general agreement with available measured data from similar systems and with comparable N inputs. Thus providing support for the simulation study as a valid way to demonstrate the potential effects of changing fertiliser management. The average of simulations run over 10 years showed that direct losses from the fertiliser were lower when the fertilisation was controlled by the proposed algorithm compared with scheduled fertilisation at similar N rates. However, with animals in the paddock and thus including the effects of urine patches, N losses were not significantly different. As there was an increase in pasture yield and consequent stocking numbers, the area receiving urinary N increased, counter balancing the increased N use efficiency when using the algorithm. Nonetheless, the larger yield lead to greater farm productivity, and this resulted in about 13% reduction in N losses per unit of milk production.

Climate-smart management can further improve winter wheat yield in China

Climate change, genotype, and agronomic management have profound impacts on crop yield. Our goal in this study is to untangle the interrelated contributions of climate change, genetic improvements, and agronomic management on winter wheat yield in China to develop management strategies that address future nutritional needs. The Agricultural Production System Simulator (APSIM) farming systems model was used to simulated long-term (1981–2010) wheat yield for four wheat production regions under different Genotype by Environment by Management (GxExM) scenarios. Using detailed field experimental data from 1981 to 2005 in conjunction with the APSIM-wheat model, the potential for climate-smart management to improve yield on a regional scale is investigated. Results showed that when all climatic variables were considered together, winter wheat relative yield change decreased from 0.62% to 7.16% over the period 1981 to 2010, depending on cultivar and growing region. The impact of individual climatic variables varied by region. In general, winter wheat yields showed the least decline in the Northern China Plain (NC) due to climate change. Cultivar renewal combined with improvements in agronomic management boosted yields but to a different extent in each region. For cultivar renewal, yields increased 6.93%, 17.69%, 24.87%, and 52.72% in the NC, Yellow and Huai River Valleys (YH), SW and YV, respectively over the period 1981 to 2010. Agronomic management improved yields by 22.91%, 5.27%, 58.77%, and 59.20% in these regions, respectively. Overall, the observed yield improvements with agronomic management were higher than those resulting from cultivar renewal for most of China's wheat growing regions. The exception was found in YH, where improvements in winter wheat yield from cultivar renewal were greater than those from agronomic management. Regardless, there is still ample room for yield improvement in winter wheat by implementing climate-smart management. SW would benefit significantly, with a potential increase of 99% because of improved agronomic management. More moderate, but still significant increases were predicted for NC and YH (49% and 42%, respectively) while only moderate improvements were anticipated for YV (17%). Our findings highlight the extent that improvements in cultivar renewal and agronomic management have compensated for the negative impacts of climate change for different wheat growing regions of China over the past three decades. The results also indicate that advances in agronomic management outweighed the effects of cultivar renewal in most regions. Climate-smart management is still needed to further improve yields in wheat-growing regions of China.

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.

Effect of variability in soil properties plus model complexity on predicting topsoil water content and nitrous oxide emissions

Despite the importance of soil physical properties on water infiltration and redistribution, little is known about the effect of variability in soil properties and its consequent effect on contaminant loss pathways. To investigate the effects of uncertainty and heterogeneity in measured soil physical parameters on the simulated movement of water and the prediction of nitrous oxide (N2O) emissions, we set up the Agricultural Production Systems sIMulator (APSIM) for different soil types in three different regions of New Zealand: the Te Kowhai silt loam and the Horotiu silt loam in the Waikato region, and the Templeton silt loam in the Canterbury region, and the Otokia silt loam and the Wingatui silt loam in the Otago region. For each of the soil types, various measured soil profile descriptions, as well as those from a national soils database (S-map) were used when available. In addition, three different soil water models in APSIM with different complexities (SWIM2, SWIM3, and SoilWat) were evaluated. Model outputs were compared with temporal soil water content measurements within the top 75mm at the various experimental sites. Results show that the profile description, as well as the soil water model used affected the prediction accuracy of soil water content. The smallest difference between soil profile descriptions was found for the Templeton soil series, where the model efficiency (NSE) was positive for all soil profile descriptions, and the RMSE ranged from 0.055 to 0.069m3/m3. The greatest difference was found for the Te Kowhai soil, where only one of the descriptions showed a positive NSE, and the other two profile descriptions overestimated measured topsoil water contents. Furthermore, it was shown that the soil profile description highly affects N2O emissions from urinary N deposited during animal grazing. However, the relative difference between the emissions was not always related to the accuracy of the measured soil water content, with soil organic carbon content also affecting emissions.

Winter Wheat Yield Gaps and Patterns in China

Core Ideas Based on yield gap analysis, wheat yield can be improved significantly in China.Both actual and potential wheat yields showed considerable spatial variability.Winter wheat yields increased in 53% of the counties in China.Nearly half of the counties experienced impaired wheat yields.Declining solar radiation and increasing temperature reduce wheat yield. Wheat (Triticum aestivum L.) yield stagnation has been reported in some regions of the world. China is the largest producer of wheat across the globe, but the pattern of its wheat yield stagnation remains poorly addressed. Here, our goal is to examine the temporal trends and spatial patterns of wheat yields along with possible causes based on a comprehensive assessment of winter wheat yield throughout China over the 31‐yr period from 1980 to 2010. Combined with the Agricultural Production Systems Simulator (APSIM) wheat model, we assessed the winter wheat yield gaps and patterns in 1414 counties and at five physiogeographic regional scales across China to ascertain the driving factors of yield variations. Wheat yields increased in 53% of the 1414 counties, but the remaining counties experienced yields that never improved, stagnated, or collapsed from 1980 to 2010. The yield gap analysis showed that actual yields represented only 59% of the national average yield potential, indicating a substantial opportunity to improve winter wheat yields. Relatively larger yield gaps were observed in the northern China Plain (NC, 47%) and in southwestern China (SW, 45%). Although the yield gaps in these regions were accompanied by significantly progressive uptrends of actual yields, our results suggest that agronomic management could be further improved. Moreover, underperforming regions could potentially benefit from new investments and strategies to reliably increase actual yields and reverse trends in stagnation in winter wheat performance.

Utilizing Collocated Crop Growth Model Simulations to Train Agronomic Satellite Retrieval Algorithms.

Due to its worldwide coverage and high revisit time, satellite-based remote sensing provides the ability to monitor in-season crop state variables and yields globally. In this study, we presented a novel approach to training agronomic satellite retrieval algorithms by utilizing collocated crop growth model simulations and solar-reflective satellite measurements. Specifically, we showed that bidirectional long short-term memory networks (BLSTMs) can be trained to predict the in-season state variables and yields of Agricultural Production Systems sIMulator (APSIM) maize crop growth model simulations from collocated Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m satellite measurements over the United States Corn Belt at a regional scale. We evaluated the performance of the BLSTMs through both k-fold cross validation and comparison to regional scale ground-truth yields and phenology. Using k-fold cross validation, we showed that three distinct in-season maize state variables (leaf area index, aboveground biomass, and specific leaf area) can be retrieved with cross-validated R2 values ranging from 0.4 to 0.8 for significant portions of the season. Several other plant, soil, and phenological in-season state variables were also evaluated in the study for their retrievability via k-fold cross validation. In addition, by comparing to survey-based United State Department of Agriculture (USDA) ground truth data, we showed that the BLSTMs are able to predict actual county-level yields with R2 values between 0.45 and 0.6 and actual state-level phenological dates (emergence, silking, and maturity) with R2 values between 0.75 and 0.85. We believe that a potential application of this methodology is to develop satellite products to monitor in-season field-scale crop growth on a global scale by reproducing the methodology with field-scale crop growth model simulations (utilizing farmer-recorded field-scale agromanagement data) and collocated high-resolution satellite data (fused with moderate-resolution satellite data).

Spatial-temporal variations of spring maize potential yields in a changing climate in Northeast China.

Based on meteorological data, agro-meteorological observations, and agricultural statistical data in Northeast China (NEC), by using the validated Agricultural Production System sIMulator (APSIM-maize), the potential, attainable, potential farmers' and actual farmers' yields of spring maize during the period 1961 to 2015 were analyzed, and the effects of climate variation on maize potential yield in NEC were quantified. Results indicated that the potential yield of spring maize was 12.2 t·hm-2 during the period 1961 to 2015, with those in northeast being lower than southwest within the study region. The attainable yield of spring maize was 11.3 t·hm-2, and showed a similar spatial distribution with potential yield. Under the current farmers' management practices, mean simulated potential and actual farmers' yields were 6.5 and 4.5 t·hm-2, respectively. Assuming there were no changes in cultivars and management practices in NEC, the mean potential, attainable, and potential farmers' yields of spring maize would decrease by 0.34, 0.25 and 0.10 t·hm-2 per decade in NEC. However, the actual farmers' yields increased with the value of 1.27 t·hm-2 per decade averaged over NEC. Due to climate variation, year-to-year variations of spring maize potential, attainable, and potential farmers' yields were significant, ranging from 10.0 to 14.4, 9.8 to 13.3, 4.4 to 8.5 t·hm-2, respectively.

Improving maize growth processes in the community land model: Implementation and evaluation

Earth system models (ESMs) are essential tools to study the impacts of historical and future climate on regional and global food production, as well as to assess the effectiveness of possible adaptations and their potential feedback to climate. Several current ESMs have the capabilities to simulate crop growth. However, some critical crop growth processes (e.g. flowering and other reproductive processes) and their responses to environmental extremes (e.g. heat stress) are not yet represented in most of these models. In this paper, an improved maize growth model was implemented in the Community Land Model version 4.5 (CLM4.5) by modifying the maize planting scheme, incorporating the phenology scheme adopted from the APSIM model (Agricultural Production Systems sIMulator), adding a new carbon allocation scheme into CLM4.5, and improving the estimation of canopy structure parameters including leaf area index (LAI) and canopy height. Unique features of the new model (CLM-APSIM) include more detailed phenology stages, an explicit implementation of the impacts of various abiotic environmental stresses (including nitrogen, water, temperature and heat stresses) on maize phenology and carbon allocation, as well as an explicit simulation of grain number. Evaluation of results at 7 AmeriFlux sites located in the US Corn Belt show that the CLM-APSIM model performs better than the original CLM4.5 in simulating phenology (LAI and canopy height), surface fluxes including gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LH), and sensible heat (SH), and especially in simulating the biomass partition and maize yield. The CLM-APSIM model corrects a serious deficiency in CLM4.5-related to CLM4.5’s underestimation of aboveground biomass (i.e. overestimation of belowground biomass) and overestimation of Harvest Index, which lead to a reasonable yield estimation with wrong mechanisms. Moreover, 13-year simulation results from 2001 to 2013 at the three Mead sites (US-Ne1, Ne2 and Ne3) show that the CLM-APSIM model can more accurately reproduce maize yield responses to growing season climate (temperature and precipitation) than the original CLM4.5 when benchmarked with the site-based observations and USDA county-level survey statistics. The CLM-APSIM model is thus more suitable than its predecessor models in terms of simulating abiotic environmental stresses on maize yield. This new model provides an improved tool to attribute maize yield change to various processes under historical and future climate, as well as to assess and design effective climate adaptation strategies for sustainable agricultural production.

Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest.

Elevated atmospheric CO2 concentrations ([CO2 ]) are expected to increase C3 crop yield through the CO2 fertilization effect (CFE) by stimulating photosynthesis and by reducing stomatal conductance and transpiration. The latter effect is widely believed to lead to greater benefits in dry rather than wet conditions, although some recent experimental evidence challenges this view. Here we used a process-based crop model, the Agricultural Production Systems sIMulator (APSIM), to quantify the contemporary and future CFE on soybean in one of its primary production area of the US Midwest. APSIM accurately reproduced experimental data from the Soybean Free-Air CO2 Enrichment site showing that the CFE declined with increasing drought stress. This resulted from greater radiation use efficiency (RUE) and above-ground biomass production at elevated [CO2 ] that outpaced gains in transpiration efficiency (TE). Using an ensemble of eight climate model projections, we found that drought frequency in the US Midwest is projected to increase from once every 5years currently to once every other year by 2050. In addition to directly driving yield loss, greater drought also significantly limited the benefit from rising [CO2 ]. This study provides a link between localized experiments and regional-scale modeling to highlight that increased drought frequency and severity pose a formidable challenge to maintaining soybean yield progress that is not offset by rising [CO2 ] as previously anticipated. Evaluating the relative sensitivity of RUE and TE to elevated [CO2 ] will be an important target for future modeling and experimental studies of climate change impacts and adaptation in C3 crops.

Modelling forage yield and water productivity of continuous crop sequences in the Argentinian Pampas

In recent years, the use of forage crop sequences (FCS) has been increased as a main component into the animal rations of the Argentinian pasture-based livestock systems. However, it is unclear how year-by-year rainfall variability and interactions with soil properties affect FCS dry matter (DM) yield in these environments. Biophysical crop models, such as Agricultural Production Systems Simulator (APSIM), are tools that enable the evaluation of crop yield variability across a wide of environments. The objective of this study was to evaluate the APSIM ability to predict forage DM yield and water productivity (WP) of multiple continuous FCS. Thirteen continuous FCS, including winter and summer crops, were simulated by APSIM during two/three growing seasons in five locations across the Argentinian Pampas. Our modelling approach was based on the simulation of multiple continuous FCS, in which crop DM yields depend on the performance of the previous crop in the same sequence and the final soil variables of the previous crop are the initial conditions for the next crop. Overall, APSIM was able to accurately simulate FCS DM yield (0.93 and 3.2 Mg ha−1 for concordance correlation coefficient [CCC] and root mean square error [RMSE] respectively). On the other hand, the model predictions were better for annual (CCC = 0.94; RMSE = 0.4 g m−2 mm−1) than for seasonal WP (CCC = 0.71; RMSE = 1.9 g m−2 mm−1), i.e. at the crop level. The model performance to predict WP was associated with better estimations of the soil water dynamics over the long-term, i.e. at the FCS level, rather than the short-term, i.e. at the crop level. The ability of APSIM to predict WP decreased as seasonal WP values increased, i.e. for low water inputs. For seasonal water inputs, <200 mm, the model tended to under-predict WP, which was directly associated with crop DM yield under-predictions for frequently harvested crops. Even though APSIM showed some weaknesses in predicting seasonal DM yield and WP, i.e. at the crop level, it appears as a potential tool for further research on complementary forage crops based on multiple continuous FCS in the Argentinian livestock systems.

Distribution of high-yield and high-yield-stability zones for maize yield potential in the main growing regions in China

Understanding the distribution of zones possessing both high yield and high yield stability (high-stable zones) for maize (Zea mays L.) yield potential is essential for optimized distribution and improvement of maize production with limited resources. In this study, the well-calibrated and validated Agricultural Production Systems sIMulator (APSIM-Maize) model with observed phenology and yield of widely planted hybrids, was applied to simulate three levels of yield potentials [radiation-temperature yield potential (Yp), climatic yield potential (Ypw) and soil-climatic yield potential (Ypws)] at 331 meteorology stations in the three main growing regions in China [the North China spring maize region (NCS), the Huanghuaihai summer maize region (HS) and the Southwest China mountain maize region (SCM)] during 1981–2010. According to the comprehensive analysis of both average and CVs for the three levels of yield potential, the high-stable zone for Yp was located in the southeastern and central portions of NCS, while both high-stable zones for Ypw and Ypws were located in eastern NCS, accounting for 16.4% and 20.0% of the area, respectively. In HS, the high-stable zone for Yp was located in the northern portions and accounted for 12.8% of the entire area in this region, while the percentages of high-stable zones for Ypw and Ypws increased to 30.4% and 35.0%, respectively, and were mainly located in the southern and eastern portions. In SCM, the high-stable zone for Yp was located in the southwestern portions and occupied 24.5% of the areas; the high-stable zones for Ypw and Ypws were found in eastern portions and accounted for 17.1% and 20.1% of the land area in this region, respectively. Yield stability was more negatively affected by rain in NCS and HS (38.9% and 34.3%, respectively) than in SCM (8.2%), while yield level was reduced by rain in more areas in SCM (19.1%). Moreover, the effects of soil on yield level and stability were limited in all the three regions.

Assessing the combined effects of climatic factors on spring wheat phenophase and grain yield in Inner Mongolia, China.

Understanding the regional relationships between climate change and crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the combined effects of climatic factors on spring wheat phenophase and grain yield over the past three decades in Inner Mongolia, China, were explored based on the daily climate variables from 1981–2014 and detailed observed data of spring wheat from 1981–2014. Inner Mongolia was divided into three different climate type regions, the eastern, central and western regions. The data were gathered from 10 representative agricultural meteorological experimental stations in Inner Mongolia and analysed with the Agricultural Production Systems Simulator (APSIM) model. First, the performance of the APSIM model in the spring wheat planting areas of Inner Mongolia was tested. Then, the key climatic factors limiting the phenophases and yield of spring wheat were identified. Finally, the responses of spring wheat phenophases and yield to climate change were further explored regionally. Our results revealed a general yield reduction of spring wheat in response to the pronounced climate warming from 1981 to 2014, with an average of 3564 kg·ha-1. The regional differences in yields were significant. The maximum potential yield of spring wheat was found in the western region. However, the minimum potential yield was found in the middle region. The air temperature and soil surface temperature were the optimum climatic factors that affected the key phenophases of spring wheat in Inner Mongolia. The influence of the average maximum temperature on the key phenophases of spring wheat was greater than the average minimum temperature, followed by the relative humidity and solar radiation. The most insensitive climatic factors were precipitation, wind speed and reference crop evapotranspiration. As for the yield of spring wheat, temperature, solar radiation and air relative humidity were major meteorological factors that affected in the eastern and western Inner Mongolia. Furthermore, the effect of the average minimum temperature on yield was greater than that of the average maximum temperature. The increase of temperature in the western and middle regions would reduce the spring wheat yield, while in the eastern region due to the rising temperature, the spring wheat yield increased. The increase of solar radiation in the eastern and central regions would increase the yield of spring wheat. The increased air relative humidity would make the western spring wheat yield increased and the eastern spring wheat yield decreased. Finally, the models describing combined effects of these dominant climatic factors on the maturity and yield in different regions of Inner Mongolia were used to establish geographical differences. Our findings have important implications for improving climate change impact studies and for local agricultural production to cope with ongoing climate change.

Measurement and simulation of water-use by canola and camelina under cool-season conditions in California

The agricultural sector of California is one of the most diverse and economically valuable in the world, but is dominated by woody perennial, and annual warm-season crops, dependent on irrigation. These face potential problems from restrictions to irrigation water supply and climate change. Canola and camelina could be used to diversify cool-season cropping in the state, but the water use of these species in the region is poorly understood. In this study, both the total and temporal water use of canola and camelina under cool-season production conditions in California were investigated using field-based and computer modeling approaches. Total and temporal water-use of both species were found to be similar to what has been observed in other regions under cool-season conditions. Observed seasonal water uptake patterns also closely matched predictions by the Agricultural Production Systems Simulator (APSIM) model. These results inform the utilization of these species as new crops in California and also contribute to estimates of water use by these globally significant oilseeds under Mediterranean to arid climate conditions.

Modelling stover and grain yields, and subsurface artificial drainage from long-term corn rotations using APSIM

The Agricultural Production Systems Simulator (APSIM) is a key tool to identify agricultural management practices seeking to simultaneously optimize agronomic productivity and input use efficiencies. The aims of this study were to validate APSIM for prediction of stover and grain yield of corn in four contrasting soils with varied N fertilizer applications (156–269kgNha−1) and to predict timing and volume from artificial subsurface drains in continuous corn and corn-soybean rotations in a silty clay loam soil at West Lafayette, IN. The APSIM validation was carried-out using a long-term dataset of corn stover and grain yields from the North Central Region of IN. The CCC (Concordance Correlation Coefficient) and SB (Simulation Bias) were used to statistically evaluate the model performance. The CCC integrates precision through Pearson’s correlation coefficient and accuracy by bias, and SB indicates the bias of the simulation from the measurement. The model demonstrated very good (CCC=0.96; SB=0%) and satisfactory (CCC=0.85; SB=2%) ability to simulate stover and grain yield, respectively. Grain yield was better predicted in continuous corn (CCC=0.73–0.91; SB=19–21%) than in corn-soybean rotations (CCC=0.56–0.63; SB=17–18%), while stover yield was well predicted in both crop rotations (CCC=0.85–0.98; SB=1–17%). The model demonstrated acceptable ability to simulate annual subsurface drainage in both rotations (CCC=0.63–0.75; SB=2–37%) with accuracy being lower in the continuous corn system than in corn-soybean rotation system (CCC=0.61-0.63; SB=9–12%). Daily subsurface drainage events were well predicted by APSIM during late spring and summer when crop water use was high, but under-predicted during fall, winter and early spring when evapotranspiration was low. Occasional flow events occurring in summer when soils were not saturated were not predicted by APSIM and may represent preferential flow paths currently not represented in the model. APSIM is a promising tool for simulating yield and water losses for corn-based cropping systems in north central Indiana US.

Evaluating water conservation effects due to cropping system optimization on the Beijing-Tianjin-Hebei plain, China

Irrigation of the conventional double cropping system of winter wheat and summer maize has caused serious problems regarding water resources and the environment on the Beijing-Tianjin-Hebei plain (HP), China. Reducing or replacing high water consumption crops (such as winter wheat) is the most impactful way to effectively save water in the HP region. In this study, crop coefficient and vegetation remote sensing data were used to estimate evapotranspiration (ETa) for winter wheat and summer maize on the HP from 2009 to 2013. For early maize, ETa and yield were based on the results from the APSIM (Agricultural Production System Simulator) crop model, which had been calibrated and validated using field experimental data. The effects on groundwater conservation and grain yield were estimated at the regional scale for the conventional winter wheat (WW) and summer maize (SM) double cropping system (Y1M2) and three alternative cropping systems: three harvests in two years system (Y2M3, 1st year: WW-SM; 2nd year: early maize), four harvests in three years system (Y3M4, 1st year: WW-SM; the next two years: early maize), as well as a continuous early maize monoculture system (Y1M1). The results showed that: (1) Relative to Y1M2, the water savings in each alternative cropping system exceeded 50% of the groundwater overdraft on the HP. The evapotranspirations of Y2M3, Y3M4 and Y1M1 decreased by 14%, 19% and 29% over that of Y1M2 (740mm), respectively. The economic water use efficiencies (WUEe) of the alternative cropping systems were 7% –16% greater than that of Y1M2. The groundwater drop by Y1M2 (0.59myear−1) was higher than that by Y2M3 (0.20myear−1) and Y3M4 (0.07myear−1), and the drop by Y1M1 was negative, indicating a groundwater rise of 0.20m. The groundwater overdraft in Y1M2 was 160mm (3.83billionm3), and the alternative cropping systems were more sustainable, with groundwater overdrafts that were less than the recharge value of 128mm on the HP. (2) In comparison with Y1M2, the total yield was reduced by 2.28, 3.04 and 4.56billionkga−1 (or 7.7%, 10.2% and 15.3%) for Y2M3, Y3M4 and Y1M1, respectively, which accounted for <15% of the regional total or <1% of the national total. It is important to note that the yield loss of the alternative cropping systems was below the net grain export amount in Hebei province, so the grain supply and demand could be roughly balanced. (3) In summary, Y2M3 is a preferred alternative cropping system over Y1M2 in the near future because of the advantages of high WUEe, economic return, mitigated groundwater level decline and balance between wheat supply and demand on the HP. Y1M1, which had the least water use and highest WUEe and economic return, is an option for the most serious groundwater overdraft area despite a contradiction between the traditional dietary habit and complete abandonment of wheat. Y3M4 is recommended as the transitional cropping system between Y2M3 and Y1M1. The method in this study is applicable to relevant regional research, and the results can be of use to the government when formulating policies on cropping systems and groundwater management.