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Organic Fertilisation, Soil Quality and Human Health pp 263–299Cite as

Decision Support Systems for Agrotechnology Transfer

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Part of the book series: Sustainable Agriculture Reviews ((SARV,volume 9))

Abstract

Climate change causing sudden floods or prolonged droughts, and global warming resulting in loss of soil carbon and an increase in temperature are two major recent concerns in agriculture. The goal of maximum possible production to feed the world’s booming population is also a priority. Maximizing crop production while mitigating the effects of climate change and global warming can only be achieved if sustainable agriculture is practiced. As agricultural management practices and views of agriculturists changed, the definition of sustainable agriculture also changed. However, conservation of environmental resources along with maximum yield of crops or cropping systems has always been the main aim of sustainable agriculture. Crop forecasting through anticipation of future weather patterns and timely decision of proper agricultural management practices to avoid sudden crop failure is one of the most advanced ways of sustainable agriculture. The suite of crop simulation models in Decision Support Systems for Agrotechnology Transfer (DSSAT) is a very useful tool for sustainable agriculture advancement. DSSAT, which is used to predict the productivity ranges of crops, also has the flexibility to use geographic information system (GIS), decision-making analysis programs and weather generators. DSSAT has been used by many scientists, decision-makers and researchers all over the world for more than two decades and has been modified to meet specific needs and improve the predictability of the crop models under different circumstances.

In this review article, the earlier phase of developments of opinions of using different crop models, details of crop models, functionalities of various programs/data management systems and the idea of evolvement of DSSAT are explained. The software package, methodology of functioning of DSSAT and its development stages are described. The article also includes (i) the advantages and scopes of using DSSAT with GIS and weather generator to predict growth, development and yields of various crops, (ii) analysis of interpretations (iii) how DSSAT may help sustain agricultural system both economically and environmentally by simulating crop systems for spatial variability, water stress situation, pest damage, climate change, risk factors and bio-energy production. The article describes researches under various adverse crop growth situations such as decline in ground water level, drought, increased temperature, CO2 elevation and interprets the results. The results in this article mainly illustrate how researchers could explore (i) the benefits of using DSSAT as an essential software, (ii) its usefulness in modeling details of soil-crop-water system functions and future potentials, and (iii) its performance as an early warning tool for determining suitable management practices to avert crop failure and stabilize the maximum production of cropping systems. The modifications made to improve the capability of the decision-making process and more accurate prediction in various modules of DSSAT and its different versions are also discussed. Researches that illustrate the comparison of DSSAT with other crop simulation models and inter-linkage of other model programs with DSSAT to address various issues of sustainable agriculture are also discussed.

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Abbreviations

AEGIS:

Agricultural and Environmental Geographical Information System

BMP:

Best Management Practice

CERES:

Crop Estimation through Resource and Environment Synthesis

CSM:

Crop Simulation Model

CTIC-TFI:

Conservation Technology Information Center and The Fertilizer Institute

CWP:

Crop Water productivity

CWU:

Crop Water Usage

DM:

Dry Matter

DSSAT:

Decision Support Systems for Agrotechnology Transfer

DSS:

Decision Support System

E:

Evaporation

ET:

Evapotranspiration

GC:

Genetic Coefficient

GCM:

General Circulation Model

GIS:

Geographic Information System

HI:

Harvest Index

IBSNAT:

International Benchmark Sites Network for Agrotechnology Transfer

ICASA:

International Consortium for Agricultural Systems Applications

N:

Nitrogen

P:

Phosphorus

RZWQM:

Root Zone Water Quality Model

SARP:

Simulation and Systems Analysis for Rice Production

SWB:

Soil Water Balance

T:

Transpiration

YVC:

Yield Variability Coefficient

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Acknowledgements

The author appreciates two anonymous reviewers for the suggestions of including few more additional recent researches for this review paper which definitely increased the quality of the paper and made it complete. The author also acknowledges a reviewer and the Editor (Dr. Eric Lichtfouse) for the ideas of putting some tables and figures for better representation of the article. The author thanks Dr. Guillermo A. Baigorria for a copy of his publication which enriched the subject matter of this article. The author is also grateful to Dr. Felix Ponder who did the proof reading of the manuscript before publication.

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  1. Agronomy and Soils, Auburn University, Extension Hall, Room-114, Auburn, AL, 36830, USA

    Reshmi Sarkar

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    Eric Lichtfouse

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Sarkar, R. (2012). Decision Support Systems for Agrotechnology Transfer. In: Lichtfouse, E. (eds) Organic Fertilisation, Soil Quality and Human Health. Sustainable Agriculture Reviews, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4113-3_10

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