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Agriculture
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Agroecosystem Modeling
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Agroecosystem Modeling

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Ecosystem, Environment and Climate Change in Agriculture".

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 10102

Special Issue Editors

Dr. Rabin Bhattarai

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: water quality; hydrology; soil erosion; sediment transport
Special Issues, Collections and Topics in MDPI journals
Dr. Hamze Dokoohaki

E-Mail Website
Guest Editor
Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
Interests: model–data fusion; process-based crop models; sequential data assimilation

Special Issue Information

Dear Colleagues,

Agroecosystem sustainability and food security have become two challenges that urgently need to be addressed. The unprecedented increase in food demand due to the growing human population has put tremendous pressure on the agriculture sector. The higher food production demand has resulted in agricultural intensification that has entailed the considerable degradation of natural resources. To obviate the offsite environmental impact and reduce tradeoffs between meeting the food demand and protecting the agroecosystem, alternative agricultural management practices must be explored. Fostering innovation in agriculture requires the integration of experimental research with new methodologies capable of assessing cause–effect relationships at different temporal and spatial scales. With recent advancements in the computational ability of different field- and watershed-scale agroecosystem models, proper and adequate evaluation and the uncertainty assessment of such tools under different soil, climatic, and management conditions remain a necessity to validate the model credibility. Further, model evaluation under varying soil and climatic conditions helps to determine the suitability and applicability of such models in assessing the effects of a wide range of management practices and climatic variability on crop production and environment. 

This Special Issue focuses on the application of agroecosystem models to answer various questions related to current and future agricultural practices to achieve agroecosystem sustainability and food security. For this reason, it welcomes highly interdisciplinary, good-quality studies from disparate research fields including crop modeling, climate–water–food nexus modeling, model–data fusion, and water quality modeling at various scales. Original research articles and reviews are accepted.

Dr. Rabin Bhattarai
Dr. Hamze Dokoohaki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • agroecosystems modeling
  • cropping systems
  • climate change
  • genotype (G) x environment (E) x management (M)
  • conservation agriculture
  • intercropping
  • remote sensing
  • crop modeling

Published Papers (5 papers)

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Research

17 pages, 3713 KiB  
Article
Analyzing the Effects of Planting Date on the Uncertainty of CERES-Maize and Its Potential to Reduce Yield Gap in Arid and Mediterranean Climates
by Mahboobe Ghobadi, Mahdi Gheysari, Mohammad Shayannejad and Hamze Dokoohaki
Agriculture 2023, 13(8), 1514; https://doi.org/10.3390/agriculture13081514 - 28 Jul 2023
Viewed by 930
Abstract
Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree [...] Read more.
Decision support system tools such as crop models and considering the uncertainties associated with them are important for making an informed decision to fill the yield gap in farms and increase food security. This study’s objective was to identify and quantify the degree to which crop management practices, as well as climate and soil, affected the uncertainty of total biomass, evapotranspiration, and water productivity of silage maize by using a crop model and spatiotemporal input data. Using a calibrated crop model (DSSAT) and pSIMS platform, three planting dates by considering ten ensemble weather data and three soil profile data were simulated for the time period between 2002 and 2017 with a 2 km × 2 km resolution across maize production areas with arid and Mediterranean climates in Isfahan province, Iran. Additionally, the findings were used to determine the yield gap in the studied area to identify opportunities to boost food production. Our results showed larger uncertainty in Mediterranean climates than in arid climates, and it was more affected by planting date than weather parameters and soil profile. The accuracy of total biomass prediction by using pSIMS-CERES-Maize based on the spatiotemporal input data was 1.9% compared to field experimental data in the dry climate, and the yield gap based on the comparison of modified-pSIMS-CERES-Maize and reported biomass was 6.8 to 13 tons ha−1 in the arid and Mediterranean climate. Generally, all results represented the importance of using crop models and considering spatiotemporal data to increase reliability and accuracy, especially in Mediterranean climates, and their potential to increase food production in developing countries with limited water resources and poor agriculture management. Full article
(This article belongs to the Special Issue Agroecosystem Modeling)
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16 pages, 4275 KiB  
Article
Evaluation of Agricultural BMPs’ Impact on Water Quality and Crop Production Using SWAT+ Model
by Shailendra Singh, Soonho Hwang, Jeffrey G. Arnold and Rabin Bhattarai
Agriculture 2023, 13(8), 1484; https://doi.org/10.3390/agriculture13081484 - 26 Jul 2023
Cited by 1 | Viewed by 1226
Abstract
Subsurface (or tile) drainage improves land productivity by enhancing soil aeration and preventing water-logged conditions. However, the continuous expansion of drained agricultural lands and reliance on synthetic fertilizer in the Midwestern United States have increasingly facilitated nitrate transport from agricultural fields to surface [...] Read more.
Subsurface (or tile) drainage improves land productivity by enhancing soil aeration and preventing water-logged conditions. However, the continuous expansion of drained agricultural lands and reliance on synthetic fertilizer in the Midwestern United States have increasingly facilitated nitrate transport from agricultural fields to surface water bodies. Hence, there is a need to implement various agricultural best management practices (BMPs) in order to reduce the adverse water quality impacts resulting from excess nitrate, such as eutrophication and the formation of hypoxic zones. In this study, we used a SWAT+ model to assess the overall impacts on the riverine nitrate load and crop yield in the corn–soybean cropping system based on a combination of different management practices. The corn and soybean yields simulated with the model were found to be in good agreement with the observed yields for both the calibration and validation periods. The long-term simulation over a period of 30 years showed a reduction in the nitrate load of up to 32% without impacting the crop yield. The model results suggest that by reducing the current N application rate by 20% and using a 40:60 split between spring pre-plant and side-dressing N applications combined with cereal rye as a cover crop in corn–soybean rotation, one can potentially reduce nitrate losses without impacting crop yields. This study will help researchers, stakeholders, and farmers to explore and adopt alternative management practices beneficial for offsetting the environmental impacts of agricultural productions on the watershed scale. Full article
(This article belongs to the Special Issue Agroecosystem Modeling)
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13 pages, 1875 KiB  
Article
Development of a Cereal–Legume Intercrop Model for DSSAT Version 4.8
by Jacques Fils Pierre, Upendra Singh, Esaú Ruiz-Sánchez and Willingthon Pavan
Agriculture 2023, 13(4), 845; https://doi.org/10.3390/agriculture13040845 - 10 Apr 2023
Cited by 4 | Viewed by 3169
Abstract
Intercropping is extensively used to increase land productivity and agricultural benefits. In developing countries, intercropping has historically been one of the most widely used cropping systems. Crop models have been used to assess risk productivity over time and space, particularly in monocropping systems. [...] Read more.
Intercropping is extensively used to increase land productivity and agricultural benefits. In developing countries, intercropping has historically been one of the most widely used cropping systems. Crop models have been used to assess risk productivity over time and space, particularly in monocropping systems. Crop models, such as the Decision Support System for Agrotechnology Transfer (DSSAT), have been widely used to improve crop growth, development, and yield predictions; however, this model has some limitations when assessing interspecific competition in intercropping systems (e.g., it does not have a subroutine capable of running two crops simultaneously). Therefore, in this study, we developed a new approach to allow DSSAT to run two crop species in intercropping systems. A light interception algorithm and modified source code were integrated into the DSSAT to simulate the relay-strip intercropping system. The intercrop model developed in this study is the first intercrop model for DSSAT. This model is generic and can be employed to build other cereal–legume intercrop models for DSSAT Version 4.8. Regarding risk assessment of crop production, the model can evaluate long-term cereal–legume intercrop yields in low-input cropping systems. Therefore, before officially launching the new model in DSSAT, more field trials are recommended to rigorously evaluate and improve the model with data from different environments. The intercrop model developed in this study is simple, so this modeling approach can be employed to develop other cereal–noncereal intercrop models. Full article
(This article belongs to the Special Issue Agroecosystem Modeling)
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21 pages, 5017 KiB  
Article
Probabilistic Assessment of Cereal Rye Cover Crop Impacts on Regional Crop Yield and Soil Carbon
by Teerath Rai, Nicole Lee, Martin Williams II, Adam Davis, María B. Villamil and Hamze Dokoohaki
Agriculture 2023, 13(1), 176; https://doi.org/10.3390/agriculture13010176 - 10 Jan 2023
Cited by 2 | Viewed by 2442
Abstract
Field research for exploring the impact of winter cover crops (WCCs) integration into cropping systems is resource intensive, time-consuming and offers limited application beyond the study area. To bridge this gap, we used the APSIM model, to simulate corn (Zea mays L.)-rye [...] Read more.
Field research for exploring the impact of winter cover crops (WCCs) integration into cropping systems is resource intensive, time-consuming and offers limited application beyond the study area. To bridge this gap, we used the APSIM model, to simulate corn (Zea mays L.)-rye (Secale cereale L.)-corn-rye and corn-rye-soybean (Glycine max L.)-rye rotations in comparison with corn-corn and corn-soybean rotations across the state of Illinois at a spatial resolution of 5 km × 5 km from 2000 to 2020 to study the impact of WCCs on soil organic carbon (SOC) dynamics and crop production. By propagating the uncertainty in model simulations associated with initial conditions, weather, soil, and management practices, we estimated the probability and the expected value of change in crop yield and SOC following WCC integration. Our results suggest that integrating cereal rye into the crop rotations imparted greater yield stability for corn across the state. It was found that the areas with low probability of increase in SOC (p < 0.75) responded equally well for soil carbon sequestration through long term adoption of WCCs. This study presents the most complete uncertainty accounting of WCC benefits across a broad region and provides greater insights into the spatiotemporal variability of WCCs benefits for increasing WCC adoption rate. Full article
(This article belongs to the Special Issue Agroecosystem Modeling)
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21 pages, 3161 KiB  
Article
Biophysical Simulation of Sheep Grazing Systems Using the SGS Pasture Model
by Andrew P. Smith
Agriculture 2022, 12(12), 2032; https://doi.org/10.3390/agriculture12122032 - 28 Nov 2022
Viewed by 1356
Abstract
The performance of farming systems models for grazed grasslands are seldom evaluated against comprehensive field data. The aim of this study was to evaluate the capacity of a daily time step, grazing systems simulation model—the SGS (Sustainable Grazing Systems) Pasture Model—to simulate production [...] Read more.
The performance of farming systems models for grazed grasslands are seldom evaluated against comprehensive field data. The aim of this study was to evaluate the capacity of a daily time step, grazing systems simulation model—the SGS (Sustainable Grazing Systems) Pasture Model—to simulate production and aspects of sustainability. This was completed by evaluating temporal changes in soil water balance, some major nitrogen (N) fluxes, as well as plant and animal production using data from two large scale experimental sites with grazing sheep. The simulations were broadly in agreement with the measurements. In cases where divergence occurred the reasons were apparent and could be explained by reference to the model structure or aspects of the field data. In particular, the simulations showed good agreement with the observed soil water, but poorer agreement with the volumes of runoff. The simulated N in leachate and soil inorganic N were less in agreement with the measured data. The model outputs were sensitive to symbiotic biological fixation by subterranean (sub) clover and mineralisation of soil organic matter, which were not measured. Similarly, there were variable results for the simulation of animal growth and production. The complexities of simulating grazing systems and comparing field observations to simulated values are discussed. Full article
(This article belongs to the Special Issue Agroecosystem Modeling)
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