The Use of Agricultural Databases for Crop Modeling: A Scoping Review

Thando Lwandile Mthembu (), Richard Kunz, Shaeden Gokool and Tafadzwanashe Mabhaudhi
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Thando Lwandile Mthembu: Centre for Water Resources Research, School of Agricultural, Earth & Environmental Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Richard Kunz: Centre for Water Resources Research, School of Agricultural, Earth & Environmental Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Shaeden Gokool: Centre for Water Resources Research, School of Agricultural, Earth & Environmental Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
Tafadzwanashe Mabhaudhi: Centre for Water Resources Research, School of Agricultural, Earth & Environmental Science, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa

Sustainability, 2024, vol. 16, issue 15, 1-20

Abstract: There is growing interest in promoting neglected and underutilized crop species to enhance agrobiodiversity and contribute to food systems transformation under climate change. A lack of available measured data has hindered the mainstreaming of these crops and limited the ability of agricultural databases to be used for calibrating and validating crop models. This study conducts a systematic scoping review and bibliometric analysis to assess the use of agricultural databases for crop modeling. The Biblioshiny App v4.1.2 and VOSviewer software v1.6.20 were used to analyze 51 peer-reviewed articles from Scopus and Web of Science. Key findings from this review were that agricultural databases have been used for estimating crop yield, assessing soil conditions, and fertilizer management and are invaluable for developing decision support tools. The main challenges include the need for high-quality datasets for developing agricultural databases and more expertise and financial resources to develop and apply crop and machine learning models. From the bibliometric dataset, only one study used modeled data to develop a crop database despite such data having a level of uncertainty. This presents an opportunity for future research to improve models to minimize their uncertainty level and provide reliable data for crop database development.

Keywords: AquaCrop; crop database; machine learning; underutilized crops; yield simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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