Production Systems Simulator Research Articles

Simulating the Response of Drought–Tolerant Maize Varieties to Nitrogen Application in Contrasting Environments in the Nigeria Savannas Using the APSIM Model

This paper assessed the application of the Agricultural Production Systems sIMulator (APSIM)–maize module as a decision support tool for optimizing nitrogen application to determine yield and net return of maize production under current agricultural practices in the Nigeria savannas. The model was calibrated for two maize varieties using data from field experiments conducted under optimum conditions in three locations during the 2017 and 2018 cropping seasons. The model was evaluated using an independent dataset from an experiment conducted under different nitrogen (N) levels in two locations within Southern and Northern Guinea savannas. The results show that model accurately predicted days to 50% anthesis and physiological maturity, leaf area index (LAI), grain yield and total dry matter (TDM) of both varieties with low RMSE and RMSEn (%) values within the range of acceptable statistics indices. Based on 31-year seasonal simulation, optimum mean grain yield of 3941 kg ha−1 for Abuja, and 4549 for Kano was simulated at N rate of 120 kg ha–1 for the early maturing variety 2009EVDT. Meanwhile in Zaria, optimum mean yield of 4173 kg ha–1 was simulated at N rate of 90 kg ha−1. For the intermediate maturing variety, IWDC2SYNF2 mean optimum yields of 5152, 5462, and 4849 kg ha−1, were simulated at N application of 120 kg ha−1 for all the locations. The probability of exceeding attainable mean grain yield of 3000 and 4000 kg ha−1 for 2009EVDT and IWDC2SYNF2, respectively would be expected in 95% of the years with application of 90 kg N ha−1 across the three sites. Following the profitability scenarios analysis, the realistic net incomes of US$ 536 ha–1 for Abuja, and US$ 657 ha−1 for Zaria were estimated at N rate of 90 kg ha−1 and at Kano site, realistic net income of US$ 720 ha–1was estimated at N rate of 120 kg ha−1 for 2009EVDT.For IWDC2SYNF2, realistic net incomes of US$ 870, 974, and 818 ha−1 were estimated at N application of 120 kg ha−1 for Abuja, Zaria, and Kano respectively. The result of this study suggests that 90 kg N ha−1 can be recommended for 2009EVDT and 120 kg N ha–1 for IWDC2SYNF2 in Abuja and Zaria while in Kano, 120 kg N ha−1 should be applied to both varieties to attain optimum yield and profit.

Does agroecosystem model improvement increase simulation accuracy for agricultural N2O emissions?

In recent decades, agroecosystem models have been developed to simulate agricultural nitrous oxide (N2O) emissions. Coefficients of determination (R2) and root mean square error (RMSE) are widely used as metrics to assess the explanatory power and simulation accuracy of models and to provide perspectives on model improvement as models evolve. This study aimed to determine whether the fitting accuracy of three agroecosystem models to simulate agricultural N2O emissions improved with advancing versions of the models. We used several quality evaluation criteria to extract 94 and 97 reported R2 and RMSE values, respectively, from 32 published articles related to the use of three of the most-used agroecosystem models in the research field of N2O emissions [i.e., DNDC (DeNitrification-DeComposition), DayCent, and APSIM (Agricultural Production Systems sIMulator)]. Results showed that there was (1) no significant improvement in simulating N2O emissions between DNDC9.3 and DNDC9.5; and (2) no significant difference between the simulation abilities of the original models and the user-defined revised models for these widely-used models. These findings may be mainly a result of the offsetting consequences of changes in publication bias and increased focus on complex agricultural issues. The study also found that the simulation accuracy of DNDC was better under conditions of higher annual mean temperature and soil bulk density and lower soil total nitrogen, mainly caused by the formulas and data used to build and validate the model. The study results suggest that the suitability of a model for simulating N2O emissions depends on the climatic and soil conditions at the location of its application. Improving the simulation accuracy of agroecosystem models will require further targeted corrective and development actions in the future.

Modeling the economic and environmental effects of corn nitrogen management strategies in Illinois

Eco-efficient use of nitrogen (N) fertilizer in Corn (Zea Mays L.) requires timely information about the supply of N from the soil and the response to N by the crop. These are complex processes, and multiple N management strategies (NMS) have been proposed over time. In this work, we used APSIM (Agricultural Production Systems sIMulator) to simulate the response of Corn's Yield to N for ca. 4200 fields in the state of Illinois in different weather scenarios. Ten different N management strategies were evaluated using environmental and economic indicators. They were created by combining different sources of trial data, different types of prediction models, different predictor variables measured on the fields, and different technologies to apply fertilizer in the field. This study provides knowledge that is unattainable through field experimentation, leveraging current knowledge on the mechanisms that drive the yield response to N to help farmers, researchers, and policymakers to select strategies in line with their objectives. Our results showed that an N management strategy that incorporates year-to-year variability in the predictions and uses data from trials spread in the region achieved the highest eco-efficiency. Such strategy would reduce the mean N-leaching by 12.7% without changing profits compared with the N management strategy most used by extension services in the area. Nitrogen measurements obtained by soil sampling were the most important predictor and could not be replaced by other less laborious variables. Variable-rate technology did not provide significant economic or environmental value in the area.

Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya.

In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project-Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning.

Plot-Scale Agroforestry Modeling Explores Tree Pruning and Fertilizer Interactions for Maize Production in a Faidherbia Parkland

Poor agricultural productivity has led to food shortages for smallholder farmers in Ethiopia. Agroforestry may improve food security by increasing soil fertility, crop production, and livelihoods. Agroforestry simulation models can be useful for predicting the effects of tree management on crop growth when designing modifications to these systems. The Agricultural Production Systems sIMulator (APSIM) agroforestry tree-proxy model was used to simulate the response of maize yield to N fertilizer applications and tree pruning practices in the parkland agroforestry system in the Central Rift Valley, Ethiopia. The model was parameterized and tested using data collected from an experiment conducted under trees and in crop-only plots during the 2015 and 2016 growing seasons. The treatments contained three levels of tree pruning (100% pruned, 50% pruned, and unpruned) as the main plots, and N fertilizers were applied to maize at two rates (9 or 78 kg N ha−1) as sub-plots. Maize yield predictions across two years in response to tree pruning and N applications under tree canopies were satisfactorily simulated (NSE = 0.72, RSR = 0.51, R2 = 0.8). Virtual experiments for different rates of N, pruning levels, sowing dates, and cultivars suggest that maize yield could be improved by applying fertilizers (particularly on crop-only plots) and by at least 50% pruning of trees. Optimal maize yield can be obtained at a higher rate of fertilization under trees than away from them due to better water relations, and there is scope for improving the sowing date and cultivar. Across a 34-year range of recent climate, small increases in yields due to optimum N-fertilizing and pruning were probably limited by nutrient limitations other than N, but the highest yields were consistently in the 2–4 m zone under trees. These virtual experiments helped to form hypotheses regarding fertilizers, pruning, and the effects of trees on soil that warrant further field evaluation.

Intelligent Personal Assistant in Business-Context: Key-feature Evaluation for User Acceptance

Abstract Background The usage of intelligent personal assistants (IPA), such as Amazon Alexa or Google Assistant is increasing significantly, and voice-interaction is relevant for workflows in a business context. Objectives This research aims to determine IPA characteristics to evaluate the usefulness of specific functions in a simulated production system of an Enterprise Resource Planning (ERP) software. A new function called explanation-mode is introduced to the scientific community and business world. Methods/Approach As part of a design science research, an artefact, i.e. an add-on for speech-interaction in business software, was developed and evaluated using a survey among ERP users and researchers. Results In the area of IPA-features, the search-function and speech input for textual fields were recognised as most useful. The newly introduced feature, the explanation mode, was positively received too. There is no significant correlation between the usefulness of features and participant-characteristics, affinity to technology or previous experience with IPAs in a private context, which is in line with previous studies in the private environment leading to the conclusion that the task attraction is the most important element for usefulness. Conclusions Most of the participants agreed that the speech-input is not able to fully substitute standard input devices, such as a keyboard or a mouse, so the IPA is recognised as an addition to traditional input methods. The usefulness is rated high especially for speech-input for long text fields, calling up masks and search-functions.

Optimizing Traditional Cropping Systems Under Climate Change: A Case of Maize Landraces and Bambara Groundnut.

Traditional crop species are reported to be drought-tolerant and nutrient-dense with potential to contribute to sustainable food and nutrition security within marginal production systems under climate change. We hypothesized that intercropping maize landraces (Zea mays L.) with bambara groundnut (Vigna subterranea (L.) Verdc.), together with optimum management strategies, can improve productivity and water use efficiency (WUE) under climate change. Using an ex-ante approach, we assessed climate change impacts and agronomic management options, such as plant ratios, and plant sequences, on yield and WUE of intercropped maize landrace and bambara groundnut. The Agricultural Production Systems sIMulator (APSIM) model was applied over four time periods; namely past (1961-1991), present (1995-2025), mid-century (2030-2060) and late-century (2065-2095), obtained from six GCMs. Across timescales, there were no significant differences with mean annual rainfall, but late century projections of mean annual temperature and reference crop evaporation (ET0) showed average increases of 3.5°C and 155mm, respectively. By late century and relative to the present, the projected changes in yield and WUE were -10 and -15% and 5 and 7% for intercropped bambara groundnut and maize landrace, respectively. Regardless of timescale, increasing plant population improved yield and WUE of intercropped bambara groundnut. Asynchronous planting increased yield and WUE for both maize landrace (5 and 14%) and bambara groundnut (35 and 47%, respectively). Most significant improvements were observed when either crop was planted 2-3 months apart. To reduce yield gaps in intercrop systems, low-cost management options like changing plant populations and sequential cropping can increase yield and WUE under projected climate change. To further increase sustainability, there is a need to expand the research to consider other management strategies such as use of other traditional crop species, fertilization, rainwater harvesting and soil conservation techniques.

Quantifying and optimizing agroecosystem services in China's Taihu Lake Basin

This study examines rice–wheat agroecosystems in the Taihu Lake Basin: one of China's largest commercial grain-farming areas and a region that has faced severe deterioration in water quality. Spatiotemporal changes over the period 1986–2015 in four key ecosystem services (ESs) – grain yield, nitrogen loss, N2O emission, and soil organic carbon (SOC) accumulation – were examined by applying the Agricultural Production Systems Simulator (APSIM) across the basin at county level. Two straw return modes (namely, full straw return versus no return) and three fertilizer-use reduction modes (−5%, −10%, and −20%) were set up to generate six combined scenarios, to propose pathways that reduce the variability of grain production and improve water quality by reducing loss of nitrogen (N loss) – in consideration of the Basin's vital role in agricultural production and the need to protect water quality. Results show that annual grain yield and net five-year difference in SOC accumulation exhibited an overall downward trend from 1986 to 2015, while N2O emission and N loss increased. Two pairs of ESs showed desirable synergies (increasing grain yield and increasing SOC accumulation; decreasing N2O emission and decreasing N loss), encompassing 45.8% and 2.4% of total cultivated land area respectively. Another two pairs exhibited desirable trade-offs (increasing SOC accumulation and decreasing N loss; increasing SOC accumulation and decreasing N2O emission), accounting for 19.0%, and 2.4% of total cultivated land area respectively. There was considerable overlap within counties, which showed high values of grain yield, N2O emission, nitrogen loss, and SOC accumulation in the Basin; but values were relatively high in the east and relatively low in the west. Fertilizer use has significant positive correlations with grain yield and SOC accumulation, and it reduces N loss and N2O emission. Straw return was predicted to raise grain yields and net five-year difference in SOC accumulation and to reduce N loss, but also to increase N2O emissions. Recommended strategies to reduce N loss and stabilize grain supply in the study area are 1) reducing fertilizer use by 20% in areas where N application was above 490 kg N/ha, and 2) implementing straw return and reducing fertilizer use by 5% for areas where N application ranged between 380 and 490 kg N/ha.

Cattle and carcass performance, and life cycle assessment of production systems utilizing additive combinations of growth promotant technologies.

The objective of this study was to determine the impact of beef production systems utilizing additive combinations of growth promotant technologies on animal and carcass performance and environmental outcomes. Crossbred steer calves (n =120) were stratified by birth date, birth weight, and dam age and assigned randomly to one of four treatments: 1) no technology (NT; control), 2) antibiotic treated (ANT; NT plus therapeutic antibiotics and monensin and tylosin), 3) implant treated (IMP; ANT plus a series of 3 implants, and 4) beta-agonist treated (BA; IMP plus ractopamine-HCl for the last 30 d prior to harvest). Weaned steers were fed in confinement (dry lot) and finished in an individual feeding system to collect performance data. At harvest, standard carcass measures were collected and the United States Department of Agriculture (USDA) Yield Grade and Quality Grade were determined. Information from the cow-calf, growing, and finishing phases were used to simulate production systems using the USDA Integrated Farm System Model, which included a partial life cycle assessment of cattle production for greenhouse gas (GHG) emissions, fossil energy use, water use, and reactive N loss. Body weight in suckling, growing, and finishing phases as well as hot carcass weight was greater (P < 0.05) for steers that received implants (IMP and BA) than non-implanted steers (NT and ANT). The average daily gain was greater (P < 0.05) for steers that received implants (IMP and BA) than non-implanted steers during the suckling and finishing phases, but no difference (P = 0.232) was detected during the growing phase. Dry matter intake and gain:feed were greater (P < 0.05) for steers that received implants than non-implanted steers during the finishing phase. Steers that received implants responded (P < 0.05) with a larger loin muscle area, less kidney pelvic and heart fat, advanced carcass maturity, reduced marbling scores, and a greater percentage of carcasses in the lower third of the USDA Choice grade. This was offset by a lower percentage of USDA Prime grading carcasses compared with steers receiving no implants. Treatments did not influence (P > 0.05) USDA Yield grade. The life cycle assessment revealed that IMP and BA treatments reduced GHG emissions, energy use, water use, and reactive nitrogen loss compared to NT and ANT. These data indicate that growth promoting technologies increase carcass yield while concomitantly reducing carcass quality and environmental impacts.

Simulating winter rye cover crop production under alternative management in a corn‐soybean rotation

AbstractThe Agricultural Production Systems sIMulator (APSIM) was used to evaluate two alternative approaches for extending the cover crop growing window into corn (Zea mays L.) and soybean (Glycine max L.) crop rotations in Nebraska, USA. We evaluated how: (i) shifting corn planting dates (mid‐April to early‐June) and (ii) altering comparative relative maturity (CRM) corn hybrids (80 to 115 days) influence cover crop biomass and corn yields over a 30‐year period. The APSIM model was tested using experimental data and was then used to simulate a range of cover crop planting and termination scenarios. Our results showed no significant yield differences within the same corn relative maturity when planted on April 20 and May 13 but that yield declined when planted in June. During a six week fall cover crop planting window (September 15–October 31), every day before October 31 that the cover crop was planted resulted in additional 62 kg ha−1 of biomass. We also simulated a one month spring termination window (April 1–April 30) and, every day delay in cover crop termination resulted in per day additional 35 kg ha−1 of biomass. Cover crop biomass accrual was highly dependent on weather, where for identical fall planting dates, a warm wet season accrued approximately four times more biomass than a cool dry season. Although we found significant yield differences between early, medium and late season CRMs, earlier fall cover crop planting associated with either earlier spring corn planting or planting an early to medium season variety leads to ten‐fold greater cover biomass. Delayed corn planting by mid‐May had no yield penalty relative to April planting, and could facilitate four‐fold greater cover crop biomass (cover crop terminated April 30 instead of April 1). Our results demonstrate that earlier cover crop planting in fall or later cover crop termination in spring can result in significantly more biomass which can be balanced with yield goals.

Monitoring and Modelling Analysis of Maize (Zea mays L.) Yield Gap in Smallholder Farming in Ghana

Modelling and multiple linear regression were used to explore the reason for low maize yield in the Atebubu-Amantin and West Mamprusi Districts of Ghana, West Africa. The study evaluated maize yields on twenty farms against measures of soil fertility, agronomic attributes and soil water availability. Correlations between yield, soil fertility, rain, crop density, and weed biomass, were low, and no single factor could explain the low yields. A 50-year virtual experiment was then set up using the Agricultural Production Systems Simulator (APSIM) to explore the interactions between climate, crop management (sowing date and nitrogen fertilization) and rooting depth on grain yield and nitrate (NO3-N) dynamics. The analysis showed that a lack of optimal sowing dates that synchronize radiation, rainfall events and nitrogen (N) management with critical growth stages explained the low farm yields.

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p &lt; 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p &lt; 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

Economic-financial analysis of the use of the detoxified castor by alkaline solutions I: production of arrays of dairy goats

We evaluated the influence of totally substituting soybean meal (SM) with detoxified castor cake (DCC) and two alkaline products on the economic and financial viability of producing Saanen and Anglo-Nubian goat dairy breeds with initial body weights of 16.22 ± 0.67 kg that had been confined until they were capable of reproduction (70% of total body weight). The goats were subjected to six simulated production systems (PS). These were PS1, rearing Saanen goats that were fed with a standard diet based on SM; PS2, rearing Anglo-Nubian goats that were fed with a standard diet based on SM; PS3, rearing Saanen goats that where fed with a standard diet based on DCC Ca(OH)2; PS4, rearing Anglo-Nubian goats that were fed with a standard diet based on DCC Ca(OH)2; PS5, rearing Saanen goats fed with a standard diet based on DCC NaOH; and PS6, rearing Anglo-Nubian goats that were fed with a standard diet based on DCC NaOH. A descriptive economic and financial evaluation of the data was carried out using AVETEC® software. The SM based diets cost approximately 58.70% and 66.22% more for the Saanen and the Anglo-Nubian goats than the other diets, respectively. The results showed that all the scenarios had a point of leveling (PL) values of less than 144 goats, which is the number of animals that can be produced annually by each system simulated in this research. The net revenue for all the systems was greater than zero, which indicated that their activities were stable and that the internal rates of return were high. The profitability indices were also very good, especially the systems that used Saanen goats, because the NPV were higher than 1 million reais. Sensitivity analyzes showed that the indicators of performance were only economically unviable under the least favorable situations, such as 30% reductions in both production and market price. The use of the DCC does not compromise the economic viability of the systems and produces higher returns than the systems that used the standard (SM) diet.

Long short-term memory neural network-based multi-level model for smart irrigation

Rice is a staple food crop around the world, and its demand is likely to rise significantly with growth in population. Increasing rice productivity and production largely depends on the availability of irrigation water. Thus, the efficient application of irrigation water such that the crop doesn’t experience moisture stress is of utmost importance. In the present study, a long short-term memory (LSTM)-based neural network with logistic regression has been used to predict the daily irrigation schedule of drip-irrigated rice. The correlation threshold of 0.75 was used for the selection of features, which helped in limiting the number of input parameters. Also, a dataset based on the recommendation of a domain expert, and another used by the tool Agricultural Production Systems Simulator (APSIM) was used for comparison. Field data comprising of weather station data and past irrigation schedules has been used to train the model. Grid search algorithm has been used to optimize the hyperparameters of the model. Nested cross-validation has been used for validating the results. The results show that the correlation-based selected dataset is as effective as the domain expert-recommended dataset in predicting the water requirement using LSTM as the base model. The models were evaluated on different parameters and a multi-criteria decision evaluation (Technique for Order of Preference by Similarity to Ideal Solution [TOPSIS]) was used to find the best performing.

Which is more important to sorghum production systems in the Sudano-Sahelian zone of West Africa: Climate change or improved management practices?

The productivity of smallholder farming systems is held back by poor soil fertility, low input levels and erratic rainfall distribution in the sorghum-based cropping systems of the Sudano-Sahelian zone of West Africa. We assessed the sensitivity of current agricultural practices to climate change and to improved management practices: (i) increased fertilizer application combined with increased plant populations and (ii) use of improved sorghum varieties. We applied the Decision Support Systems for Agro-Technological Transfer (DSSAT) Cropping Systems Model, and the Agricultural Production Systems sIMulator (APSIM), for a multiple-farm assessment (i.e. diverse types of management and soils) in Koutiala (Mali) and Navrongo (Ghana), which are representative sites for West African sorghum production systems. Baseline climate data from observed weather (1980–2009) and future climates from five Global Circulation Models (GCMs: 2040–2069) in two Representative Concentration Pathways (RCP 4.5 and 8.5) were used as inputs for crop models. In Navrongo, under current management, sorghum yields either decreased or increased compared to the baseline, depending on the crop models and the GCMs; changes in management options induced a yield increase of up to 256%. The addition of genetic improvement resulted in further yield increases (24%). In Koutiala, sorghum yield changes for future climates ranged from −38 to +8% assuming current management. Shifting to an improved cultivar had a marginal effect on grain yields, while increased fertilizer rates resulted in grain yield increases ranging of 20% and 153% for DSSAT and APSIM, respectively, assuming the current climate. We conclude that in the Sudano-Sahelian zone of West Africa sorghum, as it is cultivated today, appears moderately vulnerable to climate change, while doubling fertilizer inputs with an adjusted planting density, in the current climate, would more than double yields. However, by exploring farm diversity we established that, under certain conditions, the effect of the future climate might be as important as the effect of management changes in the current climate, hinting at the importance of locally-relevant management practices.

Economic evaluation of crop production in the Ganges region under climate change: A sustainable policy framework

Climate change has been a challenge for the planners, who aim to make sustainable policies regarding staple food security and economic sustainability of smallholder farmers. There is a dire need for a conformed policy framework that quantizes monetary losses caused by the climate change on crops and suggests response policies under realistic climatic projections. The present study develops a climate change response policy framework for Ganges river basin by projecting climatic scenarios (temperature, rainfall, potential evapotranspiration) under intergovernmental panel on climate change representative concentration pathway (RCP) 2.6 for carbon dioxide emission. The study uses a Geographical Information System (GIS) based spatiotemporal-trend approach and evaluates economic loss using Agricultural Production Systems sIMulator (APSIM) crop simulation for the Ganges river basin. The policies have been derived by using fuzzy entropy decision making method. The novelty of the study is that it evaluates impact of climate change on crop yield under multiple scenarios expectations i.e. optimistic, neutral, and pessimistic. A case study of Ganges basin, India is taken and various indices (CSI- Climate Sustainability Index and CRS- Climate Risk Security) has been introduced that can be used by the planners to implement sustainable climatic response policies. Results show that winter crops (Rabi season) in the Ganges river basin are most vulnerable to future climate change risks as the basin is expected to be heated more in winter season as compared to summer. The expected reduction in seasonal rainfall is likely to nullify any carbon fertilization benefits on winter crop yields. Crop field in south-west Ganges basin (i.e. Chambal, Betwa, Ken and Son catchments) came out to be the most vulnerable. Based on the results, sustainable crop insurance policy has been suggested.

APSIM-Tropical Pasture: A model for simulating perennial tropical grass growth and its parameterisation for palisade grass (Brachiaria brizantha)

Tropical grasses are used as forage, to produce energy from biomass, for land restoration and carbon sequestration, among other applications. Many modelling approaches have been employed to simulate tropical grasses growth, but these have several limitations that must be solved by adapting them or creating new models. This study aimed to develop a tropical pasture model in the Agricultural Production Systems Simulator (APSIM) modelling framework, and to parameterise it to simulate Brachiaria brizantha ‘BRS Piatã’ growth, under grazing and cut-and-carry management. For this, three field experiments were conducted in the South-east of Brazil where pasture growth was measured in a cut-and-carry system, with irrigated and rainfed treatments, and in a rainfed grazing system. Model evaluation was performed through precision and accuracy indices and simulation errors. Under cut-and-carry management, forage productivity was estimated with R2 values from 0.89 to 0.94, Willmott agreement indices between 0.97 and 0.98, and Nash-Sutcliffe Efficiency values of 0.88 to up to 0.92. This demonstrates the capacity of the APSIM-Tropical Pasture model to simulate tropical pastures. Simulation of phenology, early growth after sowing, partitioning and senescence during flowering, and reallocation and retranslocation of plant dry matter and nitrogen were important aspects for this capacity. Then, APSIM-Tropical Pasture can be used to simulate tropical pastures, but several requirements for further improvements have been identified, such as to improve the simulations of flowering for palisade grass, N effects on pasture yield, reallocation and retranslocation processes. Under grazing management, forage productivity was estimated with R2 = 0.80, Willmott agreement index of 0.91, and Nash-Sutcliffe Efficiency value of 0.62. Despite these reasonable results, simulations presented problems, since does not take into account the effect of grazing animals on pastures. This indicates that, in its current form, APSIM-Tropical Pasture is not able to simulate pastures under grazing effectively. However, this simulation of grazed system was important to identify main modelling constraints and direct future research to improve knowledge of processes and interactions needed for pasture model development.

The Adaptability of APSIM-Wheat Model in the Middle and Lower Reaches of the Yangtze River Plain of China: A Case Study of Winter Wheat in Hubei Province

The middle and lower reaches of the Yangtze River (MLYR) plain represent the second-largest wheat producing area in China; the winter wheat-rice system is one of the main planting systems in this region. The use of the agricultural production system simulator (APSIM)-wheat model to simulate wheat production potential and evaluate the impact of future climate change on wheat production in this region is of great importance. In this study, the adaptability of the APSIM-wheat model in the MLYR was evaluated based on observational data collected in field experiments and daily meteorological data from experimental stations in Wuhan, Jingmen, and Xiangyang in Hubei province. The results showed significant positive relationships between model-predicted wheat growth duration from sowing to anthesis and maturity and the observed values, with coefficients of determination (R2) in ranges of 0.90–0.97 and 0.93–0.96, respectively. The normalized root-mean-square error (NRMSE) of the simulated growth durations and measured values were lower than 1.6%, and the refined index of agreement (dr-values) was in the range of 0.74–0.87. The percent mean absolute relative error (PMARE) was cited here as a new index, with a value below 1.4%, indicating that the model’s rating was excellent. The model’s performance in terms of grain yield and above-ground biomass simulation was also acceptable, although it was not as good as the growth periods simulation. The R2 value was higher than 0.75 and 0.72 for the simulation of grain yield and biomass, respectively. The indices NRMSE and PMARE were lower than 19.8% and 19.9%, and the dr-value was higher than 0.71. According to our results, APSIM-wheat was an effective and accurate model for simulating the phenology and yield production processes of wheat in the MLYR, and the results also provided a theoretical basis and technical support for further research on the yield potential of wheat-rice rotation planting systems with clarification of the key factors limiting the yield gap in this region.

Simulated-based methodology for the interface configuration of cyber-physical production systems

Besides managing accruing data in production systems, Digital Twins provide additional services like the simulation or control of production systems. The Digital Twin and its real-world physical counterpart need to be interconnected using sensors and actuators to enable such services. This article proposes a simulation-based method to design and evaluate such an interface. Applying concepts from the area of software-in-the-loop, it proposes the use of a so-called Physical Twin: a simulation model which mimics the abilities of the physical system but allows simulation-based experiments to optimise the interface. After presenting the general approach, the article provides an application example of the proposed procedure. A Digital and a Physical Twin are implemented for an application scenario and connected using a simple TCP/IP interface. By varying the number of sensors as well as considering component breakdowns in the Physical Twin, this setup allows evaluating different configurations in terms of production performance. The evaluation confirms that configurations with more sensors result in higher production performance of the system. However, a saturation of the performance gain is also observable when exceeding a certain number of sensors. Therefore, determining the optimal configuration is vital to optimise the interface from a financial point of view.

Simulation and modeling of a combined biomass gasification-solar photovoltaic hydrogen production system for methanol synthesis via carbon dioxide hydrogenation

One solution to achieving a large scale distribution, transportation and storage of renewable energy is methanol production from renewable-based power plants integrated with hydrogenation. In this study, a novel non-combustion heat-carrier biomass gasifier system is proposed, coupled with a large-scale solar power plant and alkaline water electrolysis system, for methanol production from syngas, water and carbon dioxide. Aspen Plus, MATLAB and TRNSYS are used to simulate and assess the performance of each component of the proposed system, under different climatic conditions of Toronto, Canada, and Crotone, Italy. In both localities, the best energy performance that minimizes the grid energy interaction factor is obtained with a photovoltaic station of 50.4 MW coupled to biomass gasification, which leads to 0.60 kWh and 0.57 kWh, respectively, in Toronto and Crotone, of electricity sent to or drawn from the grid for each kWh required by the electrolyser. However, higher profitability may be achieved in both localities with a single biomass gasification system which brings an enhanced benefit of 0.56 M€ in Toronto and 0.44 M€ in Crotone for each kW installed. It is expected that the developed modelling approach and these four newly formulated dimensionless indicators, i.e., the satisfied load fraction, utilization factor, grid energy interaction factor, and grid economic interaction value can be used to evaluate large-scale integrated renewable-based power systems.