Soil water crop modeling for decision support in millet-based systems in the Sahel: a challenge

Based on the latest climate change projections for the 21st century, high exposure to climate change is expected in Vipava Valley, Slovenia’s sub-Mediterranean agricultural area. An irrigation-decision support system was developed and implemented on 35 farms in the period of 2016–2020 to increase agricultural climate-change resilience. Farmers have shifted from irrigation scheduling based on experience and assumptions to irrigation scheduling based on real-time soil-water monitoring to partially implement irrigation based on irrigation-decision support systems. Simulations show that if farmers continue to practice justified irrigation applications and gradually transition to replenishing soil water reservoir content to 85%, they will achieve a 25% reduction in total irrigation-volume consumption, a 24% reduction in energy requirements and a 24% reduction in CO2 emissions. Future agricultural innovation policies should extend actions beyond the financial to those facilitating the establishment of multidisciplinary agricultural innovation teams with corresponding infrastructures to better enable the mutual exchange of knowledge, learning and development of a transparent institutional framework.

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Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada

Canadian Journal of Soil Science ◽

10.4141/cjss08084 ◽

2010 ◽

Vol 90 (1) ◽

pp. 37-53 ◽

Cited By ~ 12

Author(s):

H. Wang ◽

G N Flerchinger ◽

R. Lemke ◽

K. Brandt ◽

T. Goddard ◽

...

Keyword(s):

Soil Moisture ◽

Decision Support ◽

Decision Support System ◽

Soil Water ◽

Support System ◽

Cropping System ◽

System Model ◽

Surface Residue ◽

Simultaneous Heat

The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) is a widely used modeling package that often simulates wheat yield and biomass well. However, some previous studies reported that its simulation on soil moisture was not always satisfactory. On the other hand, the Simultaneous Heat and Water (SHAW) model, a more sophisticated, hourly time step soil microclimate model, needs inputs of plant canopy development over time, which are difficult to measure in the field especially for a long-term period (longer than a year). The SHAW model also needs information on surface residue, but treats them as constants. In reality, however, surface residue changes continuously under the effect of tillage, rotation and environment. We therefore proposed to use DSSAT-CSM to simulate dynamics of plant growth and soil surface residue for input into SHAW, so as to predict soil water dynamics. This approach was tested using three conventionally tilled wheat rotations (continuous wheat, wheat-fallow and wheat-wheat-fallow) of a long-term cropping systems study located on a Thin Black Chernozemic clay loam near Three Hills, Alberta, Canada. Results showed that DSSAT-CSM often overestimated the drying of the surface layers in wheat rotations, but consistently overestimated soil moisture in the deep soil. This is likely due to the underestimation of root water extraction despite model predictions that the root system reached 80 cm. Among the eight growth/residue parameters simulated by DSSAT-CSM, root depth, leaf area index and residue thickness are the most influential characteristics on the simulation of soil moisture by SHAW. The SHAW model using DSSAT-CSM-simulated information significantly improved prediction of soil moisture at different depths and total soil water at 0-120 cm in all rotations with different phases compared with that simulated by DSSAT-CSM. Key words: Soil moisture, modeling, Decision Support System for Agrotechnology Transfer-Cropping System Model, Simultaneous Heat and Water Model

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Development and Application of Decision Support System for Agro-Technology Transfer DSSAT under Water Resources Management

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1073-1076.1596 ◽

2014 ◽

Vol 1073-1076 ◽

pp. 1596-1603

Author(s):

Yuan Shi ◽

Yi Nong Li ◽

Cheng Zhang ◽

Mei Jian Bai ◽

Yu Kun Wang

Keyword(s):

Decision Support ◽

Technology Transfer ◽

Water Resources ◽

Decision Support System ◽

Soil Water ◽

Water Resources Management ◽

Support System ◽

Cropping System ◽

System Structure ◽

Resources Management

The Cropping System Model (CMS) simulates growth, development and yield of a crop growing on a uniform area of land under prescribed or simulated management as well as the changes in soil water, carbon, and nitrogen that take place under the cropping system over time. Decision Support System for Agro-technology Transfer DSSAT is one of many cropping system models and has been relatively widely applied. In this paper, the development history, the system structure, and the application field of the model system are summarized, and the principle and mechanism of the model to simulate the soil-water balance in the study of water resources management and the representative study results obtained by the scholars in China are analyzed in detail, so as to provide references for relevant studies and applications.

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Role of Crop Modeling in scenario Development Analysis and Developing Strategic Recommendation: A Review

International Journal of Scientific Research and Management ◽

10.18535/ijsrm/v7i1.ah02 ◽

2019 ◽

Vol 7 (01) ◽

Author(s):

Tesfaye Wossen Dejenie

Keyword(s):

Decision Support ◽

Agricultural Research ◽

Growth Models ◽

Crop Growth ◽

Crop Modeling ◽

Scenario Development ◽

Management Options ◽

Field Level ◽

Crop Growth Models

The agricultural scientists and planners are facing formidable challenges to ensure continued increases in agricultural productivity to meet the food grain requirements of ever increasing population across the globe. Thus, the works on development and use of crop growth models to answer strategic and tactical questions concerning agricultural planning as well as on-farm soil and crop management are essential. Scenarios is a tool for evaluating decisions and testing policy options by indicating possible future situations which indicate the possible effects of decisions. Crop growth models are powerful tools in agricultural decision support at operational, strategic and exploratory levels. Models through the scenario analysis system plays an important role in the interface between farmers, researchers and advisors in participatory research approaches where as agricultural research, model development and testing, and application of mode-based decision support system can be mutually enhancing for better understanding and reaction future situations. This review paper is devoted to crop modeling and scenario development for planning and field level management options in crop production. This helps researchers to understand the role of crop modeling for scenario development to adjust and develop field level recommendation by considering future conditions and developing alternative strategic decisions to reduce the expected negative impact and maximize the benefit.

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ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part B. Application

Transactions of the ASABE ◽

10.13031/trans.13908 ◽

2020 ◽

Vol 63 (5) ◽

pp. 1535-1547

Author(s):

Manuel A. Andrade ◽

Susan A. O’Shaughnessy ◽

Steven R. Evett

Keyword(s):

Information System ◽

Decision Support ◽

Geographic Information System ◽

Soil Water ◽

Irrigation Management ◽

Plant Stress ◽

Geographic Information ◽

Variable Rate ◽

Center Pivot ◽

Water Sensing

HighlightsThe ARSPivot software facilitates variable-rate irrigation management of a center pivot irrigation system.The software embodies a system capable of generating site-specific prescription maps based on weather, plant, and soil water information.ARSPivot’s graphical user interface (GUI) incorporates easy-to-use geographic information system (GIS) tools that help its users to make irrigation management decisions.Abstract. The ARSPivot software was developed for the seamless operation of a complex network consisting of a variable-rate irrigation (VRI) center pivot system and an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that interfaces with weather, plant, and soil water sensing systems. ARSPivot’s graphical user interface (GUI) incorporates a built-in geographic information system (GIS) that maps a center pivot system and facilitates the analysis of data relevant to its operation. The GIS was developed following a minimalistic approach with the objective of making its geospatial data analysis tools accessible to a wide range of users (farmers, irrigation consultants, and researchers). The post-harvest analyses of two experiments carried out in the Texas High Plains during the summers of 2016 and 2017 using a three-span VRI center pivot are presented to illustrate the advantages of using ARSPivot as a decision support tool and how its GIS tools help its users make better informed decisions regarding irrigation management. In these experiments, the north-northwest (NNW) portion of a field planted with corn (Zea mays L.) was irrigated using VRI zone control, and the south-southeast (SSE) portion was irrigated using VRI speed control. Experimental plots in the NNW portion were assigned one of three irrigation levels (80%, 50%, or 30% replenishment of soil water depletion to field capacity in the top 1.5 m), and their irrigation was scheduled using either a plant stress-based algorithm implemented in the ARSPivot software or manual weekly neutron probe (NP) readings. Plots in the SSE portion were assigned a single irrigation level of 80%, and their irrigation was scheduled using either the plant stress method or a two-step hybrid approach in which soil water sensing was combined with the plant stress method to determine irrigation depths. Soil water sensing data for the ISSCADA system were provided by NP readings during the 2016 season and by sets of time-domain reflectometers (TDRs) installed at depths of 15, 30, and 45 cm during the 2017 season. No significant differences were found during either season in terms of mean dry grain yield and crop water productivity (CWP) obtained from plots irrigated at the 80% level in both sides of the field, regardless of the irrigation scheduling method or the type of VRI application method used for irrigation. No significant differences were found during either season between mean dry grain yield and CWP of plots in the NNW portion irrigated using the plant stress-based method and NP readings at the 80% irrigation level. The lack of significant differences documented the potential of the ARSPivot system as a plant and soil water sensing-based decision support software for site-specific irrigation management of corn using a VRI center pivot system. Keywords: Center pivot irrigation, Decision support system, Geographic information system, Precision agriculture, Software.

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The impact of gridded weather database on soil water availability in rice crop modeling

Theoretical and Applied Climatology ◽

10.1007/s00704-021-03906-4 ◽

2022 ◽

Author(s):

Caio Teodoro Menezes ◽

Derblai Casaroli ◽

Alexandre Bryan Heinemann ◽

Vinicius Cintra Moschetti ◽

Rafael Battisti

Keyword(s):

Soil Water ◽

Water Availability ◽

Rice Crop ◽

Soil Water Availability ◽

Crop Modeling ◽

The Impact

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PENDUGAAN DEFISIT AIR TANAMAN JARAK (RICINUS COMMUNIS L) BERDASARKAN MODEL SIMULASI DINAMIKA AIR TANAH(PREDICTION WATER DEFICIT OF CASTOR OIL (RICINUS COMMUNIS L) WITH DYNAMIC SOIL-WATER MODEL SIMULATION)

Agromet ◽

10.29244/j.agromet.19.2.1-12 ◽

2005 ◽

Vol 19 (2) ◽

pp. 1

Author(s):

F. Djufri ◽

A. Yanto ◽

. Handoko ◽

Yonny Koesmaryono

Keyword(s):

Decision Support ◽

Soil Water ◽

Dynamic Model ◽

Castor Oil ◽

Ricinus Communis ◽

Decision Support Tool ◽

Support Tool ◽

Model Soil ◽

Ricinus Communis L ◽

Plot Design

<p>Construction dynamic model soil – water that describes relationships between crop growth and development and environmental factors (weather and soil) can be further developed to be employed as a decision support tool . The objectives of the research : (1) to know interaction of factor weather , soil, castor oil crop, (2) construction dynamic model soil – water , (3) monitoring water deficit factor at level of water irrigation. The research consisted field observation and construction model. The experimental results were used to determine quantitative relationships to obtain model parameters, calibration, and validation. This research was conducted in field experimental station of Balitpa Sukamandi, and it was arranged in split plot design with three replications. Two variety of castor oil as main plot design were : (1) ASB 81, (2) ASB 60. Three levels of water irrigation as sub plot design were : (1) No water irrigation, (2) ½ ETp, (3) 1 ETp. Field measurements included weather variables, soil, and crop. The t-test does not indicate significant difference between observed and predicted soil water content. The model is valid and reasonably well for predicting soil water content as long as castor growth . The dynamic model soil-water can be employed as a decision support tool in the management of castor oil plantations in Indonesia.</p>

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FARMs: A Geospatial Crop Modeling and Agricultural Water Management System

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10080553 ◽

2021 ◽

Vol 10 (8) ◽

pp. 553

Author(s):

Jae Sung Kim ◽

Isaya Kisekka

Keyword(s):

Decision Support ◽

Open Source ◽

Management System ◽

Decision Support Tool ◽

Global Scale ◽

Complex Model ◽

Crop Modeling ◽

Web Based ◽

Support Tool ◽

Gis Technologies

To ensure agricultural sustainability and desirable environmental outcomes, stakeholders need systems-based model-driven decision support tools. The objective of this study was to develop a global scale web-based geospatial crop modeling application called Food, Agriculture, and Resource Management system (FARMs), to simplify the application of the crop simulation model —Decision Support System for Agrotechnology Transfer (DSSAT) without requiring users to create input weather, climate, and soil files. FARMs was built based on open source Geographic Information System (GIS) technologies and DSSAT to allow for adaptive management through its ability to perform in-season yield predictions for alfalfa and maize, currently. Validation of FARMs against variety trial data in California was acceptable between measured and simulated yields for alfalfa. The work done in this study showed how a complex model like DSSAT can be translated into a useable web-based decision support tool for near-real-time simulation with the help of open-source GIS technologies.

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