scholarly journals Crop Models as Decision Support Systems in Crop Production

10.5772/28976 ◽  
2012 ◽  
Author(s):  
Simone Graeff ◽  
Johanna Link ◽  
Jochen Binder ◽  
Wilhelm Claupei
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Crop Production ◽  
Support Systems ◽  
Crop Models

scholarly journals Benefits and Limitations of Decision Support Systems (DSS) with a Special Emphasis on Weeds

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 548 ◽  
Author(s):  
Panagiotis Kanatas ◽  
Ilias S. Travlos ◽  
Ioannis Gazoulis ◽  
Alexandros Tataridas ◽  
Anastasia Tsekoura ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Weed Management ◽  
Crop Production ◽  
Support Systems ◽  
Management Strategies ◽  
Farming Systems ◽  
Climatic Conditions ◽  
Management Options ◽  
The Right

Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.

Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate

2009 ◽  
Vol 60 (11) ◽  
pp. 1057 ◽  
Author(s):  
Z. Hochman ◽  
H. van Rees ◽  
P. S. Carberry ◽  
J. R. Hunt ◽  
R. L. McCown ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Crop Production ◽  
Management Practices ◽  
Support Systems ◽  
Cost Effective ◽  
Problem Definition ◽  
New Paradigm ◽  
Large Measure ◽  
Internet Service

In Australia, a land subject to high annual variation in grain yields, farmers find it challenging to adjust crop production inputs to yield prospects. Scientists have responded to this problem by developing Decision Support Systems, yet the scientists’ enthusiasm for developing these tools has not been reciprocated by farm managers or their advisers, who mostly continue to avoid their use. Preceding papers in this series described the FARMSCAPE intervention: a new paradigm for decision support that had significant effects on farmers and their advisers. These effects were achieved in large measure because of the intensive effort which scientists invested in engaging with their clients. However, such intensive effort is time consuming and economically unsustainable and there remained a need for a more cost-effective tool. In this paper, we report on the evolution, structure, and performance of Yield Prophet®: an internet service designed to move on from the FARMSCAPE model to a less intensive, yet high quality, service to reduce farmer uncertainty about yield prospects and the potential effects of alternative management practices on crop production and income. Compared with conventional Decision Support Systems, Yield Prophet offers flexibility in problem definition and allows farmers to more realistically specify the problems in their fields. Yield Prophet also uniquely provides a means for virtual monitoring of the progress of a crop throughout the season. This is particularly important for in-season decision support and for frequent reviewing, in real time, of the consequences of past decisions and past events on likely future outcomes. The Yield Prophet approach to decision support is consistent with two important, but often ignored, lessons from decision science: that managers make their decisions by satisficing rather than optimising and that managers’ fluid approach to decision making requires ongoing monitoring of the consequences of past decisions.

Crop growth models for decision support systems

1996 ◽  
Vol 76 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Y. W. Jame ◽  
H. W. Cutforth
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Crop Production ◽  
Systems Approach ◽  
Support Systems ◽  
Growth Models ◽  
Crop Growth ◽  
Agricultural Management ◽  
Knowledge Based ◽  
Crop Growth Models

Studies on crop production are traditionally carried out by using conventional experience-based agronomic research, in which crop production functions were derived from statistical analysis without referring to the underlying biological or physical principles involved. The weaknesses and disadvantages of this approach and the need for greater in-depth analysis have long been recognized. Recently, application of the knowledge-based systems approach to agricultural management has been gaining popularity because of our expanding knowledge of processes that are involved in the growth of plants, coupled with the availability of inexpensive and powerful computers. The systems approach makes use of dynamic simulation models of crop growth and of cropping systems. In the most satisfactory crop growth models, current knowledge of plant growth and development from various disciplines, such as crop physiology, agrometeorology, soil science and agronomy, is integrated in a consistent, quantitative and process-oriented manner. After proper validation, the models are used to predict crop responses to different environments that are either the result of global change or induced by agricultural management and to test alternative crop management options.Computerized decision support systems for field-level crop management are now available. The decision support systems for agrotechnology transfer (DSSAT) allows users to combine the technical knowledge contained in crop growth models with economic considerations and environmental impact evaluations to facilitate economic analysis and risk assessment of farming enterprises. Thus, DSSAT is a valuable tool to aid the development of a viable and sustainable agricultural industry. The development and validation of crop models can improve our understanding of the underlying processes, pinpoint where our understanding is inadequate, and, hence, support strategic agricultural research. The knowledge-based systems approach offers great potential to expand our ability to make good agricultural management decisions, not only for the current climatic variability, but for the anticipated climatic changes of the future. Key words: Simulation, crop growth, development, management strategy

scholarly journals Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements

2020 ◽  
Vol 12 (23) ◽  
pp. 3945
Author(s):  
Massimo Tolomio ◽  
Raffaele Casa
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Water Stress ◽  
Decision Support Systems ◽  
Growth And Development ◽  
Support Systems ◽  
Irrigation Scheduling ◽  
Crop Growth ◽  
Plant Water ◽  
Crop Models

Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations.

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