Simulating water and nitrogen runoff with APSIM

Vogeler, Iris; Cichota, Rogerio; Langer, Stephanie; Thomas, S; Ekanayake, Dinanjana; Werner, A.

doi:10.1016/j.still.2022.105593

Cited by 10 publications

(4 citation statements)

References 41 publications

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“…Not only the plant processes but also performance in livestock farms were also assessed. The main purpose of some of the publications was to study the soil-related processes [14,[46][47][48][49][50], and in most of the publications, it was either documented as supportive output to evaluate any other plantrelated processes. The developed database can be used in selecting crop models/supporting modules for various types of applications, such as soil nutrient dynamics, GHG emissions, and other soil physicochemical applications in different geographic regions.…”

Section: Discussionmentioning

confidence: 99%

“…For example, these crop models were used to study climate change impacts and adaptation [8,9], agroforestry systems [10], greenhouse gas emissions [11], farm economics [12], farming system simulations [5], and many more [5,13]. In addition, several attempts were reported on the simulation of different soil-related processes and parameters using crop models; nitrogen dynamics [14,15], carbon dynamics [16,17], greenhouse gas emission [18,19], soil water and water use efficiency [20,21], other physicochemical properties [22][23][24] and as part of a decision support system for farming [5,25]. Integration of crop models for mapping and yield estimation at the country/regional level is gaining popularity, which includes soil as input data [26].…”

Section: Introductionmentioning

confidence: 99%

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Process-Based Crop Models in Soil Research: A Bibliometric Analysis

et al. 2023

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Different types of soil data are used in process-based crop models as input data. Crop models have a diverse range of applications, and soil research is one of them. This bibliographic analysis was conducted to assess the current literature on soil-related applications of crop models using two widely used crop models: Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT). The publications available in the Scopus database during the 2000–2021 period were assessed. Using 523 publications, a database on the application of process-based crop models in soil research was developed and published in an online repository, which is helpful in determining the specific application in different geographic locations. Soil-related applications on APSIM and DSSAT models were found in 41 and 43 countries, respectively. It was reported that selected crop models were used in soil water, physical properties, greenhouse gas emissions, N leaching, nutrient dynamics, and other physical and chemical properties related to applications. It can be concluded that a crop model is a promising tool for assessing a diverse range of soil-related processes in different geographic regions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

et al. 2023

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“…The description of soil processes included in most simulation models allows them to be used to predict soil conditions and the dynamics of soil components [14]. In agroecosystem modeling tasks, it is of interest to predict the dynamics of nutrient elements and evaluate their loss as a result of washing out from the soil profile [15,16]. Most models analyze soil profiles by separate horizons, between which substance and energy exchange takes place [17].…”

Section: Introductionmentioning

confidence: 99%

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Mirpulatov,

Gasanov,

Matveev

2023

Agronomy

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Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively.

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“…It can be used to assess the impact of temperature changes on crop yields and inform adaptation strategies. Vogeler et al [28] found that for well-drained soils in regions with high precipitation and no water limitations, the APSIM model displays low sensitivity to soil hydraulic parameters and suggests that general data from databases may be justifiable instead of relying solely on site-specific measurements of hydraulic properties. Research by Wimalasiri et al [29] highlights the potential of selecting specific cultivars and adjusting planting dates as climate change adaptation strategies based on APSIM crop model simulation results.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Climate Change Effects on Wheat Production under Different Representative Concentration Pathway Scenarios in North Kazakhstan

Teleubay,

Yermekov,

Rustembayev

et al. 2023

Sustainability

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Adverse weather conditions, once rare anomalies, are now becoming increasingly commonplace, causing heavy losses to crops and livestock. One of the most immediate and far-reaching concerns is the potential impact on agricultural productivity and global food security. Although studies combining crop models and future climate data have been previously carried out, such research work in Central Asia is limited in the international literature. The current research aims to harness the predictive capabilities of the CRAFT (CCAFS Regional Agricultural Forecasting Toolbox) to predict and comprehend the ramifications stemming from three distinct RCPs, 2.6, 4.5, and 8.5, on wheat yield. As a result, the arid steppe zone was found to be the most sensitive to an increase in greenhouse gases in the atmosphere, since the yield difference between RCPs 2.6 and 8.5 accounted for almost 110 kg/ha (16.4%) and for 77.1 kg/ha (10.4%) between RCPs 4.5 and 8.5, followed by the small hilly zone with an average loss of 90.1 and 58.5 kg/ha for RCPs 2.6–8.5 and RCPs 4.5–8.5, respectively. The research findings indicated the loss of more than 10% of wheat in the arid steppe zone, 7.6% in the small hilly zone, 7.5% in the forest steppe zone, and 6% in the colo steppe zone due to climate change if the modeled RCP 8.5 scenario occurs without any technological modernization and genetic modification. The average wheat yield failure in the North Kazakhstan region accounted for 25.2, 59.5, and 84.7 kg/ha for RCPs 2.6–4.5, 4.5–8.5, and 2.6–8.5, respectively, which could lead to food disasters at a regional scale. Overall, the CRAFT using the DSSAT crop modeling system, combined with the climate predictions, showed great potential in assessing climate change effects on wheat yield under different climate scenarios in the North Kazakhstan region. We believe that the results obtained will be helpful during the development and zoning of modified, drought-resistant wheat varieties and the cultivation of new crops in the region.

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Simulating water and nitrogen runoff with APSIM

Cited by 10 publications

References 41 publications

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Comparison of Climate Change Effects on Wheat Production under Different Representative Concentration Pathway Scenarios in North Kazakhstan

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