Simulation of pest effects on crops using coupled pest-crop models: the potential for decision support

1998 ◽  
pp. 221-266 ◽  
Author(s):  
P. S. Teng ◽  
W. D. Batchelor ◽  
H. O. Pinnschmidt ◽  
G. G. Wilkerson
Keyword(s):  
Decision Support ◽  
Crop Models

scholarly journals Evaluation of Different Crop Models for Simulating Rice Development and Yield in the U.S. Mississippi Delta

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1905
Author(s):  
Sanai Li ◽  
David Fleisher ◽  
Dennis Timlin ◽  
Vangimalla R. Reddy ◽  
Zhuangji Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mississippi Delta ◽  
Rice Yield ◽  
Growing Season ◽  
The United States ◽  
Delta Region ◽  
Growth Duration ◽  
Cold Temperatures ◽  
Crop Models ◽  
General Response

The United States is one of the top rice exporters in the world, but warming temperatures and other climate trends may affect grain yield and quality. The use of crop models as decision support tools for a climate impact assessment would be beneficial, but suitability of models for representative growing conditions need to be verified. Therefore, the ability of CERES-Rice and ORYZA crop models to predict rice yield and growing season duration in the Mississippi Delta region was assessed for two widely-grown varieties using a 34-year database. CERES-Rice simulated growth duration more accurately than ORYZA as a result of the latter model’s use of lower cardinal temperatures. An increase in base and optimal temperatures improved ORYZA accuracy and reduced systematic error (e.g., correlation coefficient increased by 0.03–0.27 and root mean square error decreased by 0.3–1.9 days). Both models subsequently showed acceptable skill in reproducing the growing season duration and had similar performance for predicting rice yield for most locations and years. CERES-Rice predictions were more sensitive to years with lower solar radiation, but neither model accurately mimicked negative impacts of very warm or cold temperatures. Both models were shown to reproduce 50% percentile yield trends of more than 100 varieties in the region for the 34-year period when calibrated with two representative cultivars. These results suggest that both models are suitable for exploring the general response of multiple rice cultivars in the Mississippi Delta region for decision support studies involving the current climate. The response of rice growth and development to cold injury and high temperature stress, and variation in cultivar sensitivity, should be further developed and tested for improved decision making tools.

scholarly journals Canopy Temperature as a Key Physiological Trait to Improve Yield Prediction under Water Restrictions in Potato

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1436
Author(s):  
Johan Ninanya ◽  
David A. Ramírez ◽  
Javier Rinza ◽  
Cecilia Silva-Díaz ◽  
Marcelo Cervantes ◽  
...  
Keyword(s):  
Decision Support ◽  
Stomatal Conductance ◽  
Mean Squared Error ◽  
Satellite Image ◽  
Model Performance ◽  
Canopy Temperature ◽  
Coefficient Of Determination ◽  
Yield Prediction ◽  
Crop Models ◽  
Water Treatments

Canopy temperature (CT) as a surrogate of stomatal conductance has been highlighted as an essential physiological indicator for optimizing irrigation timing in potatoes. However, assessing how this trait could help improve yield prediction will help develop future decision support tools. In this study, the incorporation of CT minus air temperature (dT) in a simple ecophysiological model was analyzed in three trials between 2017 and 2018, testing three water treatments under drip (DI) and furrow (FI) irrigations. Water treatments consisted of control (irrigated until field capacity) and two-timing irrigation based on physiological thresholds (CT and stomatal conductance). Two model perspectives were implemented based on soil water balance (P1) and using dT as the penalizing factor (P2), affecting the biomass dynamics and radiation use efficiency parameters. One of the trials was used for model calibration and the other two for validation. Statistical indicators of the model performance determined a better yield prediction at harvest for P2, especially under maximum stress conditions. The P1 and P2 perspectives showed their highest coefficient of determination (R2) and lowest root-mean-squared error (RMSE) under DI and FI, respectively. In the future, the incorporation of CT combining low-cost infrared devices/sensors with spatial crop models, satellite image information, and telemetry technologies, an adequate decision support system could be implemented for water requirement determination and yield prediction in potatoes.

Defining irrigation thresholds in remote sensing-based decision support systems: a review of crop models mechanistic descriptions of crop water stress

2020 ◽  
Author(s):  
Massimo Tolomio ◽  
Raffaele Casa
Keyword(s):  
Remote Sensing ◽  
Decision Support ◽  
Water Stress ◽  
Biomass Growth ◽  
Hydrological Models ◽  
Crop Water ◽  
Stress Indices ◽  
Crop Models ◽  
Wilting Point ◽  
Crop Water Stress

<p>Irrigation management decision support systems based on remote sensing and hydrological models need to find a balance between simplicity and accuracy in the definition of crop water stress thresholds when irrigation should be triggered. Among the most widely used crop models, which synthesize current mechanistic knowledge of crop water stress processes, there is a wide range of complexity that is worth exploring in order to improve the formalisms of current hydrological models.</p><p>In the present work, some of the most widely used crop models (chosen among those freely available and well documented) were examined in their description of crop water stress processes and irrigation thresholds definition. They are: APSIM, AQUACROP, CROPSYST, CROPWAT, DAISY, DSSAT, EPIC, STICS and WOFOST. Model manuals and scientific papers were reviewed to identify differences and similarities in the water stress functions related to crop growth.</p><p>A strict categorization of the model features is inappropriate, since the functions utilized are always at least slightly different and the models may focus on different features of the agroecosystem. Nevertheless, major similarities and differences among the models were found:</p><ol><li><em>The function of biomass growth.</em> AQUACROP and CROPWAT (both developed by FAO) are water-driven models (growth is directly related to transpiration). DAISY, DSSAT, EPIC, STICS and WOFOST are radiation-driven models (growth is related to radiation). APSIM and CROPSYST calculate both water- and radiation-driven biomass and keep the most limiting of these.</li> <li><em>The main variable used to calculate water stress indices.</em> AQUACROP, CROPWAT and WOFOST use stress coefficients that depend directly on the depletion status of plant available water (difference between field capacity and wilting point). CROPSYST, DAISY, DSSAT and EPIC calculate water stress on the ratio between actual transpiration (limited by roots and soil characteristics) and potential transpiration (weather-dependent). APSIM uses both approaches, depending on the specific crop and growth process targeted. STICS expresses the transpiration rate as a function of the available water content (in m<sup>3</sup>/m<sup>3</sup> above wilting point), and from this it calculates water stress indices.</li> <li><em>The influence of water stress indices on vegetative growth.</em> Water stress in CROPWAT, DAISY and WOFOST affects biomass growth, whereas in APSIM, AQUACROP, CROPSYST, DSSAT, EPIC and STICS multiple indices affect biomass growth and leaf expansion in different ways. The rationale behind the last approach is that as soil water uptake becomes more difficult, water stress slows down cells division and expansion (reducing the leaf expansion rate) before photosynthesis is reduced by stomatal closure.</li> </ol><p>The models were then calibrated for the maize and tomato crops using field and remote sensing data on crop yield, soil moisture, evapotranspiration (ET) and leaf area index (LAI), for two locations, respectively in Northern and Southern Italy (Calcinato and Capitanata). Simulations were then carried out and compared in terms of the optimal irrigation amounts calculated by the different models and predicted yields.</p>

The IBSNAT Project and Agricultural Experimentation in Developing Countries

1990 ◽  
Vol 26 (4) ◽  
pp. 369-380 ◽  
Author(s):  
S. R. Harrison ◽  
P. K. Thornton ◽  
J. B. Dent
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Field Experiments ◽  
Farming Systems ◽  
Data Sets ◽  
Crop Models ◽  
Field Experimentation ◽  
Crop Management Systems ◽  
The Tropics

SUMMARYThe International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) project has established an international network of co-operating farming systems researchers. A decision support system that includes a number of crop models and databases has been developed to assist in the evaluation of new agrotechnology packages in the tropics and sub-tropics. Standards have been established for the recording of ‘minumum data sets’ from field experiments to allow the crop models to be validated for specific sites. The decision support system, still under active development, has the potential to reduce substantially the time and cost of field experimentation necessary for adequate evaluation of new cultivars and new crop management systems.

scholarly journals Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Agriculture ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1166
Author(s):  
Aftab Wajid ◽  
Khalid Hussain ◽  
Ayesha Ilyas ◽  
Muhammad Habib-ur-Rahman ◽  
Qamar Shakil ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Goodness Of Fit ◽  
Crop Improvement ◽  
Wheat Yield ◽  
Agricultural System ◽  
Area Index ◽  
Crop Models ◽  
Mean Square Errors

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.

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 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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