Crop Models as Decision Support Systems in Crop Production

Graeff, Simone; Link, Johanna; Binder, Jochen; Claupein, Wilhelm

doi:10.5772/28976

Cited by 12 publications

(15 citation statements)

References 51 publications

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“…The average percentage difference was within the 5 % range for both maize and triticale; hence, the model errors and RMSE are in line with other studies [13,26,27]. This indicates a general suitability of the CERES-Maize and CERES-Wheat models to simulate maize and triticale yields under the given environmental conditions.…”

Section: Dry Matter Yieldssupporting

confidence: 86%

“…Based on given climatic (temperature, precipitation, solar radiation) and soil (soil water, soil nitrogen) conditions, the crop simulation model (CSM)-CERES-maize/wheat simulates daily growth, development, and yield of an applied crop [13,14]. The model is part of the Decision Support System for Agrotechnology Transfer (DSSAT 4.0) [12].…”

Section: Model Descriptionmentioning

confidence: 99%

“…It provides various application possibilities, such as studying management options including fertilizer impact, irrigation or pest management on crop growth and yield in a region of interest, and the simulation of future climate forecasts especially with respect to climate change on crop yields [12]. Political factors can also be represented, for instance, regulations regarding the maximum amount of N fertilizer applications or available subsidies linked to crop management requirements [13].…”

Section: Introductionmentioning

confidence: 99%

“…Process-orientated models such as Decision Support System for Agrotechnology Transfer (DSSAT) [12] are widely used as a tool to identify the interrelations between predominant agro-environmental conditions and crop management, as well as yield determining, limiting, or reducing factors [13]. It provides various application possibilities, such as studying management options including fertilizer impact, irrigation or pest management on crop growth and yield in a region of interest, and the simulation of future climate forecasts especially with respect to climate change on crop yields [12].…”

Section: Introductionmentioning

confidence: 99%

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Using a Crop Growth Model to Quantify Regional Biogas Potentials: an Example of the Model Region Biberach (South-West Germany)

2014

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Over the last decade, political framework conditions in the energy sector provoked a strong focus on biogas production in Germany. In this context, a sufficient and secure regional biomass supply is needed in order to run biogas plants economically. It is important to estimate which biomass amounts can be produced and are available for bioenergy production in a defined region. The present study focused on a model-based approach quantifying the biomass and, from this, the resulting biogas potential of the model region of Biberach (south-west Germany) using the process-oriented crop growth model DSSAT 4.0. Considering the regional soil and climate conditions of the model region, dry matter yields of maize, triticale, and a crop rotation system (CRS) of maize and triticale including different management systems (change in sowing and harvest date) were simulated. The results indicated an adequate model fit between simulated and measured yields. Dry matter yields of maize (14.7 t ha −1 ), triticale (12.7 t ha −1 ), and the CRS (18.1-19.2 t ha −1 ) differed significantly, indicating that the chosen CRS provided the highest dry matter yields. The biomass potential of all crops was simulated considering different bioenergy scenarios depending on the available agricultural land used for bioenergy. The highest biomass potential was provided by the management system consisting of maize and triticale sown on 1 May and 15 October, respectively. Finally, an additional energy potential of 45,000 kW el (bioenergy scenario 50/50 % of the agricultural land used for biogas production) and of 5,700 kW el (bioenergy scenario 25/25 % of the agricultural land used for biogas production) was determined for the county of Biberach by implementing a CRS, which consisted of maize and triticale.It could be concluded that an additional biomass potential for biogas production exists in the county. Suitable areas for the location of biogas plants could be identified based on the available biomass potential.

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Section: Dry Matter Yieldssupporting

confidence: 86%

Section: Model Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using a Crop Growth Model to Quantify Regional Biogas Potentials: an Example of the Model Region Biberach (South-West Germany)

2014

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“…According to Graeff et al (2012), information to guide best management practices is widely available. However, the challenge for a farmer is to determine how to use the information with respect to the type of management decisions to be made and the current risk.…”

Section: Introductionmentioning

confidence: 99%

Assessment of sorghum–cowpea intercrop system under waterlimited conditions using a decision support tool

Chimonyo

Modi

Mabhaudhi

2016

WSA

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Intercropping can improve crop productivity through increased water use efficiency (WUE). However, limited information exists to support its adoption and subsequent management. In such instances, crop models can be used as decision support tools to complement data from field trials. The Agricultural Production Systems Simulator Model (APSIM) was used to develop best management practices for improved yield and WUE for a sorghum-cowpea intercrop system for 5 sites in KwaZulu-Natal, South Africa: Richards Bay, Umbumbulu, Deepdale, Wartburg and Ukulinga. Each site represented 1 of 5 different bio-resource units. Planting dates (trigger season climate method, modelling and fixed date approaches), fertilizer rates (0, 50 and 100% recommended N rate), plant population (50% less and 50% more, for either sorghum or cowpea) and irrigation (deficit irrigation and rainfall-based approaches) were considered. In Deepdale, planting dates generated by the model gave high (952.2±85 and 326.3±68 kg•ha -1 ) and stable yields for sorghum and cowpea, respectively. Adding 100% fertilizer improved both yield and WUE of the intercrop by 18.5 and 5.1%, respectively, in Umbumbulu and Wartburg. Across all environments, sorghum and cowpea plant populations of 39 000 and 13 000 plants•ha -1 , respectively, increased yield (26.11%) and WUE (15.54%) of the intercrop system. Deficit irrigation was more effective resulting in yield (12.84%) and WUE (11.09%) improvements. It is concluded that APSIM can be used to develop best management practices to assist in developing guidelines for improving productivity of intercrop systems under water-scarce conditions.

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Biomass yield and energy balance of Virginia fanpetals in different production technologies in north-eastern Poland

Jankowski

Dubis

Sokólski

et al. 2019

Energy

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Crop Models as Decision Support Systems in Crop Production

Cited by 12 publications

References 51 publications

Using a Crop Growth Model to Quantify Regional Biogas Potentials: an Example of the Model Region Biberach (South-West Germany)

Using a Crop Growth Model to Quantify Regional Biogas Potentials: an Example of the Model Region Biberach (South-West Germany)

Assessment of sorghum–cowpea intercrop system under waterlimited conditions using a decision support tool

Biomass yield and energy balance of Virginia fanpetals in different production technologies in north-eastern Poland

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