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Agricultural system modeling: current achievements, innovations, and future roadmap

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Abstract

Agricultural system models are tools that provide a synthesis and quantification to evaluate the effects of water, soil, crops, management practices, and climate on the sustainability of agricultural production and to ensure food security. Present-day agricultural models are the outcomes of research initiatives started 3–4 decades ago. However, existing models are not fully equipped with the important advancements achieved in the field of data and information and computer technology (ICT). The majority of the existing models are still using the old programming languages and legacy codes, software testing is uncommon while maintenance of documentation and software/codes is also a neglected avenue. These deficiencies could be rectified through better data harmonization and interlinking of models by developing different frameworks such as BioMA (Biophysical Model Applications) and APSIM (the Agricultural Production Systems Simulator). These developments assist in data compatibility by creating a common vocabulary and datasets for model ensembling. For next-generation modeling, gaps in the existing data should be minimized, a transition from supply-driven approach to demand-driven approach is needed to develop models according to the demands of end-users. Finally, focus on the software design and development should be encouraged in the modeling community as ICT has opened new horizons in the form of parallel processing or cloud computing methods, software languages and coding standards, and the development of user-friendly community-driven mobile applications that will enable the use of models to a more divergent group of stakeholders. Overall, agricultural systems modeling needs to rapidly adopt new technologies such as ICT, big data, remote sensing, and machine learning algorithms that will help enhance crop models’ accuracy and efficiency in designing sustainable agricultural systems at different farms, landscape, regional, and continental scales to meet the future demands of end-users.

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Abbreviations

AEZ:

Agro-ecological zoning

AgMIP:

Agricultural Model Inter-comparison and Improvement Project

APSIM:

The Agricultural Production Systems Simulator

BioMA:

Biophysical Model Applications

CGIAR:

The Consultative Group on International Agricultural Research

CNCPS:

Cornell Net Carbohydrate and Protein System

DSSAT:

Decision Support System for Agro-technology Transfer

FAO:

Food and Agriculture Organization

GCMs:

General circulation models

GHG:

Greenhouse gas emissions

GTAP:

Global Trade and Analysis Project

IBM:

International Business Machines Cooperation

IBSNAT :

International Benchmark Sites Network for Agro-technology Transfer

ICASA:

International Consortium for Agricultural Systems Applications

ICT:

Information and computer technology

INRA:

French National Institute for Agronomic Research

IPCC:

Intergovernmental Panel on Climate Change

IPM:

Integrated pest management

IRRI:

International Rice Research Institute

MAXENT:

Maximum Entropy Model

SEAMLESS:

System for Environmental and Agricultural Modeling: Linking European Science and Society Systems

SARP:

Systems Analysis of Rice Production

USAID:

United States Agency for International Development

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Funding

The study is supported by the grants from the Ministry of Science and Technology, China (QN2021046001L), National Natural Science Foundation of China (42171042), Tianshan Innovation Team (2020D14042) and the Tianshan Youth Project (2019Q086).

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Authors and Affiliations

  1. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang, 830011, China

    Zeeshan Ahmed, Dongwei Gui, Yi Liu & Yunfei Liu

  2. Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Xinjiang, 848300, China

    Zeeshan Ahmed, Dongwei Gui, Yi Liu & Yunfei Liu

  3. University of Chinese Academy of Sciences, Beijing, China

    Dongwei Gui, Yi Liu & Yunfei Liu

  4. Department of Bioresource Engineering, McGill University, Saitne-Anne-de-Bellevue, Québec, H9X 3V9, Canada

    Zhiming Qi

  5. Institute of Geographical Information Systems (IGIS), School of Civil and Environmental Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Muhammad Azmat

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  1. Zeeshan Ahmed
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  2. Dongwei Gui
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Conceptualization (Zeeshan Ahmed and Dongwei Gui), original draft preparation (Zeeshan Ahmed), and review and editing (Zhiming Qi, Yi Liu, Muhammad Azmat, and Yunfei Liu).

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Correspondence to Dongwei Gui.

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Ahmed, Z., Gui, D., Qi, Z. et al. Agricultural system modeling: current achievements, innovations, and future roadmap. Arab J Geosci 15, 363 (2022). https://doi.org/10.1007/s12517-022-09654-7

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