Research

13 pages, 292 KiB

Open AccessArticle

A Decision Support System to Estimate Green Sustainability from Environmental Protection and Debt Financing Indicators

by Zhengyu Ren, Hsing Hung Chen, Kunseng Lao and Hongyi Zhang

Agriculture 2022, 12(8), 1249; https://doi.org/10.3390/agriculture12081249 - 18 Aug 2022

Viewed by 1473

In the social context of advocating a low-carbon economy, achieving sustainable growth in line with current social development requirements is an issue that agribusiness must face. In order to explore the mechanisms influencing the sustainable growth of Chinese agriculture and to optimize the [...] Read more.

In the social context of advocating a low-carbon economy, achieving sustainable growth in line with current social development requirements is an issue that agribusiness must face. In order to explore the mechanisms influencing the sustainable growth of Chinese agriculture and to optimize the quality of agribusiness decisions, this paper examines the relationship between environmental management, debt financing indicators, and financial sustainable growth of the company in Chinese agriculture. Specifically, a decision support system based on the least square dummy variable (LSDV) model, mediating effects model and threshold effects model was constructed by using annual financial reports and questionnaire data of the listed agricultural enterprises. After empirical analysis, the following results were obtained: first, both environmental management and debt financing management help Chinese agricultural firms achieve financially sustainable growth. Second, debt financing can transmit the effect of environmental management on financially sustainable growth. Third, there are significant differences in the effects of debt financing on financially sustainable growth under different environmental management conditions. Finally, in order to promote the development of Chinese agriculture, this paper suggests that agricultural enterprises should actively implement environmental management and that relevant Chinese authorities should lower the financing threshold of the agricultural industry, while ensuring risk regulation. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

20 pages, 1731 KiB

Open AccessArticle

Simulating Spring Barley Yield under Moderate Input Management System in Poland

by Elzbieta Czembor, Zygmunt Kaczmarek, Wiesław Pilarczyk, Dariusz Mańkowski and Jerzy H. Czembor

Agriculture 2022, 12(8), 1091; https://doi.org/10.3390/agriculture12081091 - 25 Jul 2022

Cited by 3 | Viewed by 2494

Abstract

In recent years, forecasting has become particularly important as all areas of economic life are subject to very dynamic changes. In the case of agriculture, forecasting is an essential element of effective and efficient farm management. Factors affecting crop yields, such as soil, [...] Read more.

In recent years, forecasting has become particularly important as all areas of economic life are subject to very dynamic changes. In the case of agriculture, forecasting is an essential element of effective and efficient farm management. Factors affecting crop yields, such as soil, weather, and farm management, are complex and investigations into the relation between these variables are crucial for agricultural studies and decision-making related to crop monitoring, with special emphasis for climate change. Because of this, the aim of this study was to create a spring barley yield prediction model, as a part of the Advisory Support platform in the form of application for Polish agriculture under a moderate input management system. As a representative sample, 20 barley varieties, evaluated under 13 environments representative for Polish conditions, were used. To create yield potential model data for the genotype (G), environment (E), and management (M) were collected over 3 years. The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. On average, the precision of the cultivar yielding forecast (expressed as a percentage), based on the independent traits, was 78.60% (Model F-statistic: 102.55***) and the range, depending of the variety, was 89.10% (Model F-statistic: 19.26***)–74.60% (Model F-statistic: 6.88***). The model developed using Multiple Linear Regression (MLR) simulated barley yields with high goodness of fit to the measured data across three years of evaluation. It was possible to observe a large differentiation for the response to agroclimatic or soil factors. Under Polish conditions, ten traits have a similar effect (in the prediction model, they have the same sign: + or -) on the yield of almost all varieties (from 17 to 20). Traits that negatively affected final yield were: lodging tendency for 18 varieties (18-), sum of rainfall in January for 19 varieties (19-), and April for 17 varieties (17-). However, the sum of rainfall in February positively affected the final yield for 20 varieties (20+). Average monthly ground temperature in March positively affected final yield for 17 varieties (17+). The average air temperature in March negatively affected final yield for 18 varieties (18-) and for 17 varieties in June (17-). In total, the level of N + P + K fertilization negatively affected the final yield for 15 varieties (15-), but N sum fertilization significantly positively affected final yield for 15 varieties (15+). Soil complex positively influenced the final yield of this crop. In the group of diseases, resistance to powdery mildew and rhynchosporium significantly decreased the final yield. For Polish conditions, it is a complex model for prediction of variety in the yield, including its genetic potential. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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30 pages, 6285 KiB

Open AccessArticle

Developing Visual-Assisted Decision Support Systems across Diverse Agricultural Use Cases

by Nyi-Nyi Htun, Diego Rojo, Jeroen Ooge, Robin De Croon, Aikaterini Kasimati and Katrien Verbert

Agriculture 2022, 12(7), 1027; https://doi.org/10.3390/agriculture12071027 - 14 Jul 2022

Cited by 5 | Viewed by 2027

Abstract

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise [...] Read more.

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty and are evaluated with end-users. To address these gaps, we developed six web-based visual-assisted DSSs for various agricultural use cases, including biological efficacy correlation analysis, water stress and irrigation requirement analysis, product price prediction, etc. We then evaluated our DSSs with domain experts, focusing on usability, workload, acceptance and trust. Results showed that our systems were easy to use and understand, and participants perceived them as highly performant, even though they required a slightly high mental demand, temporal demand and effort. We also published the source code of our proposed systems so that they can be re-used or adapted by the agricultural community. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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25 pages, 8649 KiB

Open AccessFeature PaperArticle

Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study

by Jeroen Ooge and Katrien Verbert

Agriculture 2022, 12(7), 1024; https://doi.org/10.3390/agriculture12071024 - 14 Jul 2022

Cited by 1 | Viewed by 1867

Abstract

The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are [...] Read more.

The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual decision support system that incorporates price prediction, uncertainty, and visual analytics techniques. We evaluated our prototype with 10 participants who are active in different parts of agrifood. Through semi-structured interviews and questionnaires, we collected quantitative and qualitative data about four metrics: usability, usefulness and needs, model understanding, and trust. Our results reveal that the first three metrics can directly and indirectly affect appropriate trust, and that perception differences exist between people with diverging experience levels in predictive modelling. Overall, this suggests that user-centred approaches are key for increasing uptake of visual decision support systems in agrifood. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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16 pages, 2024 KiB

Open AccessArticle

Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques

by José Escorcia-Gutierrez, Margarita Gamarra, Roosvel Soto-Diaz, Meglys Pérez, Natasha Madera and Romany F. Mansour

Agriculture 2022, 12(7), 977; https://doi.org/10.3390/agriculture12070977 - 07 Jul 2022

Cited by 9 | Viewed by 2332

Abstract

Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil nutrients depending upon the fertility level [...] Read more.

Soil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil nutrients depending upon the fertility level and crop requirements. Since the conventional soil nutrient testing models are not feasible in real time applications, an efficient soil nutrient, and potential of hydrogen (pH) prediction models are essential to improve overall crop productivity. In this aspect, this paper aims to design an intelligent soil nutrient and pH classification using weighted voting ensemble deep learning (ISNpHC-WVE) technique. The proposed ISNpHC-WVE technique aims to classify the existence of nutrients and pH levels exist in the soil. In addition, three deep learning (DL) models namely gated recurrent unit (GRU), deep belief network (DBN), and bidirectional long short term memory (BiLSTM) were used for the predictive analysis. Moreover, a weighted voting ensemble model was employed which allows a weight vector on every DL model of the ensemble depending upon the attained accuracy on every class. Furthermore, the hyperparameter optimization of the three DL models was performed using manta ray foraging optimization (MRFO) algorithm. For investigating the enhanced predictive performance of the ISNpHC-WVE technique, a comprehensive simulation analysis takes place to examine the pH and soil nutrient classification performance. The experimental results showcased the better performance of the ISNpHC-WVE technique over the recent techniques with accuracy of 0.9281 and 0.9497 on soil nutrient and soil pH classification. The proposed model can be utilized as an effective tool to improve productivity in agriculture by proper soil nutrient and pH classification. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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15 pages, 2208 KiB

Open AccessArticle

A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers

by Juan D. Borrero and Jesús Mariscal

Agriculture 2022, 12(6), 767; https://doi.org/10.3390/agriculture12060767 - 27 May 2022

Cited by 14 | Viewed by 8072

Abstract

New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite [...] Read more.

New players are entering the new and important digital data market for agriculture, increasing power asymmetries and reinforcing their competitive advantages. Although the farmer remains at the heart of agricultural data collection, to date, only a few farmers participate in data platforms. Despite this, more and more decision support systems (DSSs) tools are used in agriculture, and digital platforms as data aggregators could be useful technologies for helping farmers make better decisions. However, as these systems develop, the efficiency of these platforms becomes more challenging (sharing, ownership, governance, and transparency). In this paper, we conduct a case study for an accessible and scalable digital data platform that is focused on adding value to smallholders. The case study research is based on meta-governance theory and multidimensional multilayered digital platform architecture, to determine platform governance and a data development model for the Andalusian (Spain) fruit and vegetable sector. With the information obtained from the agents of this sector, a digital platform called farmdata was designed, which connects to several regional and national, and public and private databases, aggregating data and providing tools for decision making. Results from the interviews reflect the farmer’s interests in participating in a centralized cloud data platform, preferably one that is managed by a university, but also with attention being paid toward security and transparency, as well as providing added value. As for future directions, we propose further research on how the benefits should be distributed among end users, as well as for the study of a distributed model through blockchain. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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26 pages, 8171 KiB

Open AccessArticle

LASAM Model: An Important Tool in the Decision Support System for Policymakers and Farmers

by Irina Pilvere, Aleksejs Nipers, Agnese Krievina, Ilze Upite and Daniels Kotovs

Agriculture 2022, 12(5), 705; https://doi.org/10.3390/agriculture12050705 - 17 May 2022

Cited by 3 | Viewed by 2510

Abstract

Today’s global food system (including production, transportation, processing, packing, storage, retail sale, consumption, losses and waste) provides income to more than a billion people all over the world and makes up a significant part of many countries’ economies. The 21st century’s food systems [...] Read more.

Today’s global food system (including production, transportation, processing, packing, storage, retail sale, consumption, losses and waste) provides income to more than a billion people all over the world and makes up a significant part of many countries’ economies. The 21st century’s food systems that bring food from “farm to fork” face various challenges, including a shortage of agricultural land and water, competition with the energy industry, changes in consumption preferences, a rising global population, negative effects of climate change, etc. Therefore, many countries are working on creating various models to function as an important decision support system tool for policymakers, farmers and other stakeholders. Various agricultural sector models see particularly extensive use in the European Union (EU), determining the impact of the Common Agricultural Policy (CAP) and helping to create future development scenarios. This is why a special model adapted to the national conditions, called LASAM (Latvian Agricultural Sector Analysis Model), was created in Latvia, making it possible to use historical data on the development of agricultural sectors, medium-term price projections for agricultural products in the EU, changes in support policy, as well as the necessity for the resources used to project the long-term (up to 2050) development of agriculture. The LASAM model covers the crop sector, the animal sector and the overall socioeconomic development, as well as the growth of organic farming and greenhouse gas (GHG) emissions. This paper discusses the main objectives achieved in developing a decision support tool and presenting the research results: LASAM was used to prepare projections of the possible development of Latvia’s principal sectors of agriculture until 2050, considering the necessity to reduce GHG emissions, made available through the LASAM web application. Given that the projection data obtained by LASAM are public, they can be used (1) for national policy making in rural business development, which affects the development of the economy as a whole; and (2) internationally, to compare the projections made in Latvia with those obtained through various agricultural sector models and projected development trends. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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30 pages, 12098 KiB

Open AccessEditor’s ChoiceArticle

A Spatial Feature-Enhanced Attention Neural Network with High-Order Pooling Representation for Application in Pest and Disease Recognition

by Jianlei Kong, Hongxing Wang, Chengcai Yang, Xuebo Jin, Min Zuo and Xin Zhang

Agriculture 2022, 12(4), 500; https://doi.org/10.3390/agriculture12040500 - 31 Mar 2022

Cited by 69 | Viewed by 7465

Abstract

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to [...] Read more.

With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applications is essentially the fine-grained image classification task, which aims to learn effective discriminative features that can identify the subtle differences among similar visual samples. It is still challenging to solve for existing standard models troubled by oversized parameters and low accuracy performance. Therefore, in this paper, we propose a feature-enhanced attention neural network (Fe-Net) to handle the fine-grained image recognition of crop pests and diseases in innovative agronomy practices. This model is established based on an improved CSP-stage backbone network, which offers massive channel-shuffled features in various dimensions and sizes. Then, a spatial feature-enhanced attention module is added to exploit the spatial interrelationship between different semantic regions. Finally, the proposed Fe-Net employs a higher-order pooling module to mine more highly representative features by computing the square root of the covariance matrix of elements. The whole architecture is efficiently trained in an end-to-end way without additional manipulation. With comparative experiments on the CropDP-181 Dataset, the proposed Fe-Net achieves Top-1 Accuracy up to 85.29% with an average recognition time of only 71 ms, outperforming other existing methods. More experimental evidence demonstrates that our approach obtains a balance between the model’s performance and parameters, which is suitable for its practical deployment in precision agriculture art applications. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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22 pages, 995 KiB

Open AccessArticle

Web-Based Integer Programming Decision Support System for Walnut Processing Planning: The MeliFen Case

by Carlos F. Brunner-Parra, Luis A. Croquevielle-Rendic, Carlos A. Monardes-Concha, Bryan A. Urra-Calfuñir, Elbio L. Avanzini and Tomás Correa-Vial

Agriculture 2022, 12(3), 430; https://doi.org/10.3390/agriculture12030430 - 19 Mar 2022

Cited by 2 | Viewed by 3156

Abstract

Chile is among the largest walnut producers and exporters globally, thanks to a favorable nut growth and production environment. Despite an increasingly competitive market, the literature offers little scientific advice regarding decision support systems (DSSs) for the nut sector. In particular, the literature [...] Read more.

Chile is among the largest walnut producers and exporters globally, thanks to a favorable nut growth and production environment. Despite an increasingly competitive market, the literature offers little scientific advice regarding decision support systems (DSSs) for the nut sector. In particular, the literature does not present optimization approaches to support decision-making in walnut supply chain management, especially the processing planning. This work provides a DSS that allows the exporter to plan walnut processing decisions taking into account the quality of the raw material, such as size, color, variety, and external and internal defects, in order to maximize the benefits of the business. To formalize the problem, an integer programming model is proposed. The DSS was implemented via a web application for MeliFen, a walnut exporter located near Santiago, Chile. A comparative analysis of the last two years revealed that MeliFen increased its profit by approximately 9.8% using this tool. We also suggest other uses that this DSS provides, besides profit maximization. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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18 pages, 5542 KiB

Open AccessArticle

Stability Analysis of a Sprayer UAV with a Liquid Tank with Different Outer Shapes and Inner Structures

by Shibbir Ahmed, Huang Xin, Muhammad Faheem and Baijing Qiu

Agriculture 2022, 12(3), 379; https://doi.org/10.3390/agriculture12030379 - 08 Mar 2022

Cited by 11 | Viewed by 2989

Abstract

The performance of sprayer UAVs largely depends on accurate trajectory control while spraying. A large amount of a liquid payload may create a sloshing effect inside the liquid tank, which may occur largely during hazardous phenomena, such as wind gusts and obstacle avoidance. [...] Read more.

The performance of sprayer UAVs largely depends on accurate trajectory control while spraying. A large amount of a liquid payload may create a sloshing effect inside the liquid tank, which may occur largely during hazardous phenomena, such as wind gusts and obstacle avoidance. This all-way sloshing force inside the tank may disturb the UAV’s trajectory by, for example, a displacement from the planned path or collision with an obstacle. A large number of existing sprayer UAVs already carry various-shaped tanks. A UAV’s liquid-sloshing problem must be reduced for existing and future plant protection. Applying suitable methods can achieve these goals and provide better performance. Moreover, various tank models have different structures and capabilities, which must be fixed using a flexible solution. This article proposes a simple baffle solution for all forms of pesticide tanks and compares baffle systems’ impacts using primary shaped tanks. Indoor lab experiments showed the extreme impacts inside the tanks. Outdoor UAV mission experiments provided the practical effectiveness of the tank structures, and primary shaped tank comparison results provided guidance for future UAV pesticide-tank manufacturing. A new baffle ball design is presented for a universal solution. A one-axis linear slider was used for optical observations, an open-source flight controller was used for on-field compliance, and plenty of tests were done to prove the concept and show the efficiency. The flat hexagonal tank and baffle ball system showed better results in both indoor and outdoor experiments. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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22 pages, 3423 KiB

Open AccessFeature PaperArticle

A Digital Advisor Twin for Crop Nitrogen Management

by Fabian Weckesser, Michael Beck, Kurt-Jürgen Hülsbergen and Sebastian Peisl

Agriculture 2022, 12(2), 302; https://doi.org/10.3390/agriculture12020302 - 21 Feb 2022

Cited by 1 | Viewed by 2950

Abstract

Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a [...] Read more.

Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha⁻¹ to 16 kg N ha⁻¹ when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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14 pages, 3769 KiB

Open AccessFeature PaperArticle

Model for Predicting Rice Yield from Reflectance Index and Weather Variables in Lowland Rice Fields

by Chinaza B. Onwuchekwa-Henry, Floris Van Ogtrop, Rose Roche and Daniel K. Y. Tan

Agriculture 2022, 12(2), 130; https://doi.org/10.3390/agriculture12020130 - 18 Jan 2022

Cited by 8 | Viewed by 3699

Abstract

Smallholder rice farmers need a multi-purpose model to forecast yield and manage limited resources such as fertiliser, irrigation water supply in-season, thus optimising inputs and increasing rice yield. Active sensing tools like Canopeo and GreenSeeker-NDVI have provided the opportunity to monitor crop health [...] Read more.

Smallholder rice farmers need a multi-purpose model to forecast yield and manage limited resources such as fertiliser, irrigation water supply in-season, thus optimising inputs and increasing rice yield. Active sensing tools like Canopeo and GreenSeeker-NDVI have provided the opportunity to monitor crop health and development at different growth stages. In this study, we assessed the effectiveness of in-season estimation of rice yield in lowland fields of northwest Cambodia using weather data and vegetation cover information measured with; (1) the mobile app-Canopeo, and (2) the conventional GreenSeeker hand-held device that measures the normalised difference vegetative index (NDVI). We collected data from a series of on-farm field experiments in the rice-growing regions in 2018 and 2019. Average temperature and cumulative rainfall were calculated at panicle initiation and pre-heading stages when the crop cover index was measured. A generalised additive model (GAM) was generated using log-transformed data for grain yield, with the combined predictors of canopy cover and weather data during panicle initiation and pre-heading stages. The pre-heading stage was the best stage for grain yield prediction with the Canopeo-derived vegetation index and weather data. Overall, the Canopeo index model explained 65% of the variability in rice yield and Canopeo index, average temperature and cumulative rainfall explained 5, 65 and 56% of the yield variability in rice yield, respectively, at the pre-heading stage. The model (Canopeo index and weather data) evaluation for the training set between the observed and the predicted yield indicated an R² value of 0.53 and root mean square error (RMSE) was 0.116 kg ha⁻¹ at the pre-heading stage. When the model was tested on a validation set, the R² value was 0.51 (RMSE = 925.533 kg ha⁻¹) between the observed and the predicted yield. The NDVI-weather model explained 62% of the variability in yield, NDVI, average temperature and cumulative rainfall explained 3, 62 and 54%, respectively, of the variability in yield for the training set. The NDVI-weather model evaluation for the training set showed a slightly lower fit with R² value of 0.51 (RMSE = 0.119 kg ha⁻¹) between the observed and the predicted yield at pre-heading stage. The accuracy performance of the model indicated an R² value of 0.46 (RMSE = 979.283 kg ha⁻¹) at the same growth stage for validation set. The vegetation-derived information from Canopeo index-weather data increasingly correlated with rice yield than NDVI-weather data. Therefore, the Canopeo index-weather model is a flexible and effective tool for the prediction of rice yield in smallholder fields and can potentially be used to identify and manage fertiliser and water supply to maximise productivity in rice production systems. Data availability from more field experiments are needed to test the model’s accuracy and improve its robustness. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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22 pages, 2944 KiB

Open AccessEditor’s ChoiceArticle

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

by Aftab Wajid, Khalid Hussain, Ayesha Ilyas, Muhammad Habib-ur-Rahman, Qamar Shakil and Gerrit Hoogenboom

Agriculture 2021, 11(11), 1166; https://doi.org/10.3390/agriculture11111166 - 19 Nov 2021

Cited by 12 | Viewed by 2906

Abstract

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were [...] Read more.

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha⁻¹ with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments. Full article

(This article belongs to the Special Issue Application of Decision Support Systems in Agriculture)

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