cropsimulationmodelinvegetableproduction-Credit Seminar.pptx

Crop simulation models have become indispensable tools in modern agriculture for predicting plant growth, yield and resource use across diverse agro-ecological zones. In the domain of vegetable science, which deals with a wide range of short-duration, high-value crops with diverse growth habits and environmental requirements, these models are critical for planning, risk management, and improving production efficiency.
Vegetable crops exhibit highly variable phenological responses to climate, nutrient inputs, and irrigation. Crop models such as DSSAT-CROPGRO, SUBSTOR, and APSIM have been successfully adapted to simulate growth dynamics of major vegetables including tomato (Solanum lycopersicum), potato (Solanum tuberosum), cabbage (Brassica oleracea), and onion (Allium cepa) (Singh et al., 2015; Peter et al., 2008). These models integrate crop physiology—such as photosynthesis, transpiration, partitioning of assimilates—and environmental data to evaluate different management scenarios. For instance, CROPGRO-Tomato has demonstrated accuracy in simulating the influence of nitrogen levels and planting dates on fruit yield and quality under open-field and protected cultivation systems (Boote et al., 1998; Singh & Naik, 2020).
From the perspective of vegetable science, models also contribute significantly to understanding complex phenomena like flowering behavior, fruit set, nutrient uptake kinetics, and water use efficiency in various crops (Hazra et al., 2011). As vegetable crops are often sensitive to abiotic stresses (e.g., high temperature, salinity, drought), simulation models are employed to predict stress impacts and guide the development of climate-resilient cultivars and production strategies (Chadha, 2001; Pandey et al., 2020).

1. 15-10-2025 Vegetable Science 1 To Credit Seminar

2. THE CLIMATE IS CHANGING, SO SHOULD WE! AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 2

3. Source: WMO, 2022 3 AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science Global Mean Temperature Difference from 1850-2025

4. 4 AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science Source: WMO, 2022

5. 5 Department of Horticulture College of Agriculture, Jodhpur Agriculture University, Jodhpur Chairperson : Dr. S.K. Moond Designation: Professor (Hort.) College of Agriculture, Jodhpur CROP SIMULATION MODELS IN VEGETABLE PRODUCTION Presented by : Kamini Parashar Class: Ph.D. 1 st Year (Hort.) Vegetable Science

6. Content AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 6

7. Introduction Crop modeling involves the use of computational techniques to simulate the growth, development and yield of crops under various environmental conditions. In vegetable production, crop modeling helps predict how different factors such as weather, soil and management practices influence crop growth and yield. Crop modeling AGRICULTURE UNIVERSITY, JODHPUR G owtham S and Manibharti 15-10-2025 Vegetable Science 7

8. Properties 8 Part of reality Relatively autonomous Self organizing Viable, sustainable & perfoming Need of Crop Models Traditional approach Novel approach Crop models Experimental trial and error Use of manipulations & experiments that are too expensive & lengthy Address dynamic complexity Identification best management strategies Predict & project long term effects of climatic variability Allow hypothetical situations to be investigated AGRICULTURE UNIVERSITY, JODHPUR G owtham S and Manibharti 15-10-2025 Vegetable Science

9. P. De Reffye & M. Jaeger, CIRAD AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 9

10. Cournede P.H. et al. , 2006 (France) AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 10

11. Chronology of Crop Simulation Modeling AGRICULTURE UNIVERSITY, JODHPUR YEAR   DEVELOPMENTS 1960 Simple Water-balance models 1965 Models for photosynthetic rate of crop canopies (de Wit)  1970 ELCROS (Elementary Crop Growth Simulator construction) de Wit et al . 1977 Introduction of micrometeorology in the models & quantification of crop canopy resistance ( Goudriaan ) 1978 BACROS (Basic Crop Growth Simulator) (de Wit and Goudriaan ) 1982 DSSAT (Decision Support System for Agro -Technology Transfer) at University of Hawaii by IBSNAT 1987 SIMRIW (Simulation Model for Rice-Weather relations) by T. Horie (IRRI) 1990 APSIM (Agricultural Production Systems sIMulator ) by CSIRO and University of Queensland 1990 CropSyst by a team at Washington State University's Department of Biological Systems Engineering 1994 India’s 1st crop model WTGROWS at IARI followed by ORYZA1N by Kropff et al. 15-10-2025 Vegetable Science 11

12. Applications of Crop Models 12 AGRICULTURE UNIVERSITY, JODHPUR Research on Interaction of Plant, Soil, Weather and Management Practices Prediction of Crop Growth as well as Limiting factors On farm decision making and agronomic management Optimizing management using climatic predictions Precision Farming and Site Specific Experimentation Weather Based agro advisory services Yield analysis and Forecasting Introduction and Breeding of New Varieties Policy Management 15-10-2025 Vegetable Science

13. Impact of Modelling on Agriculture 13 AGRICULTURE UNIVERSITY, JODHPUR Evaluation cultivar stability under long term weather conditions Evaluation of optimum management for cultural practices in crop production Evaluation weather risk via weather forecasting Proper crop surveillance with respect to pests, diseases and deficiency & excess of nutrients Yield prediction and forecasting These are resource conserving tools Solved various practical problems in agriculture Identification of the precise reasons for yield gap at farmer’s field Forecasting crop yields 15-10-2025 Vegetable Science

14. Different Types of Crop Modelling STATISTIC MODEL DETERMINISTIC MODELS DYNAMIC MODELS STOCHASTIC MODEL DESCRIPTIVE MODEL SIMULATION MODELS STATIC MODELS MECHANISTIC MODELS EXPLANATORY MODEL SIMULATION MODELS AGRICULTURE UNIVERSITY , JODHPUR 15-10-2025 Vegetable Science 14

15. Crop Simulation Model Simulation models involve Computer models with a mathematical represent of a real world system. One of the main goals of crop simulation models is to estimate agricultural production as a function of weather and soil conditions as well as crop mgmt. These form a group of models that is designed for the purpose of imitating the behaviour of a system . This model uses one or more differential equation over time normally from planting until harvest 1 2 3 4 AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 15

16. Steps in Modeling AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 16

17. Aspect Crop Simulation Models (Process-Based) Other Types of Crop Models (Statistical, Hybrid) Complexity High complexity due to detailed representation of physiological processes. Lower complexity, often based on empirical relationships. Representation Detailed simulation of crop growth and development based on physiological mechanisms. Relies on statistical relationships between input and output variables. Input Requirements Require extensive input data including weather, soil, and management practices. May require less extensive input data, primarily historical records. Interpretabilit y Provides insights into underlying biological processes and environmental interactions. Results may lack interpretability regarding underlying mechanisms. Flexibility Flexible in capturing diverse cropping systems and management practices. Flexibility may vary depending on the specific algorithm used. Validation and Uncertainty Require rigorous validation due to their mechanistic nature; uncertainties can arise. Validation may be simpler, but uncertainties in predictions may still exist. Comparison with other crop models AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 17

18. Importance of CSMs Quantitative Understanding: CSMs provide a quantitative understanding of how different factors affect crop growth and productivity. Climate Change Resilience: They help assess the impact of climate change on crop yield and food security. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 18

19. Need of CSMs in Vegetable Production AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 19

20. Inputs and System Approach of Crop Simulation Models Weather Parameters Soil parameter Crop Characteristics Management Practices ..etc. Yield Prediction Flowering Time Crop biomass over time Leaf Area Index  Water Stress... etc. Outputs Inputs Crop Model Patil , et al. (2019 ) AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 20

21. Different Types of CSMs use in Vegetable Production AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 21

22. Yield prediction for crop management Water and irrigation management Soil fertility management Adaptive management using climate forecasts Precision agriculture The Decision Support System for Agro-technology Transfer (DSSAT) is an application software program that includes crop simulation models for more than 42 crops to make more reliable predictions. Its includes several models for simulating vegetable crops such as tomatoes, peppers, cucurbits, and leafy greens . These models, including CROPGRO, SUBSTOR, and CSM-CROPGRO-Vegetable , simulate the growth, development, and yield of vegetable crops under varying environmental conditions (soil and plant water) and management practices (nitrogen and carbon balances). DSSAT was developed by Dr. Gerrit Hoogenboom at the University of Florida Applications of DSSAT Climate variability and Climate change Soil carbon sequestration Land use change analysis Plant breeding and Genotype DSSAT (Decision Support System for Agro -technology Transfer) AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 22

23. VegSyst VegSyst is a crop simulation model specifically designed for vegetable production , focusing on crops like tomatoes and peppers . It assists growers in making informed decisions regarding irrigation scheduling to optimize water use efficiency and maximize crop yield and quality. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 23

24. STICS ( Simulateur multidisciplinary pour les Cultures Standard) STICS is a crop simulation model developed by INRAE (French National Research Institute for Agriculture, Food, and the Environment) STICS is a process based crop model with a daily time step. The model simulates crop growth, soil water and nitrogen balances. Climate, soil, and crop management data are required to run the model. It has adaptability to various vegetable crops such as tomato, spinach, carrots, lettuce, beetroot, pea, and rapeseed etc. It is used for research and decision support in vegetable production systems. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 24

25. Crop Gro-Garden It is used to assess the impact of interventions and technologies on vegetable productivity, food security, and nutrition . CropGro -Garden allows users to assess the impact of various management practices, such as irrigation scheduling, fertilizer application, and planting density, on the productivity and food security of vegetable crops. It takes into account factors such as climate, soil properties, crop characteristics, and management practices to simulate crop growth and yield under different scenarios. CROPGRO was developed by Dr. Gerrit Hoogenboom and his colleagues at the USDA ARS. The model is part of the Decision Support System for Agrotechnology Transfer (DSSAT), a suite of crop models used for agricultural research and decision support. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 25

26. ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) ALMANAC is a plant-oriented process-based model that simulates a large variety of crops and grasses. This model is sensitive to changes in soil properties, weather, and cropping management that affect water and nutrient supplies to plants. The model operates on a daily time step. The model simulates competition for light, water, and nutrients between plant species The model uses more than 50 plant parameters representative of various crops, grasses, shrubs, and trees. A set of parameters for each of the studied vegetables , including bush bean, green bean, peppermint, spearmint, cabbage, straight neck squash, zucchini, and bell pepper. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 26

27. Agricultural Production Systems Simulator (APSIM) The Agricultural Production Systems Simulator is comprehensive model developed to simulate biophysical processes in farming systems, particularly as it relates to the economic and ecological outcomes of management practices in the face of climate risk. It can simulate the growth, development, and yield of various vegetable crops under different climates, soils, and management scenarios. Applications  Support for on-farm decision making,  Farming systems design for production or resource management  Assessment of the value of seasonal climate forecasting  Analysis of supply chain issues in agribusiness  Development of waste management guidelines  Risk assessment for policy making  As a guide for research and educational activities AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 27

28. WOFOST (World Food Study) is a mechanistic model Use: Crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions. WOFOST can be used to estimate crop production, indicate yield variability, evaluate the effects of climate changes or soil fertility changes. Crops: wheat, maize, barley, rice, sugar beet, potato, field bean, soybean and sunflower. The successive WOFOST versions (version 3.1) (version 4.1) (version 4.3) ( version 4.4) 28 AGRICULTURE UNIVERSITY, JODHPUR WOFOST Model 15-10-2025 Vegetable Science

29. SALUS (System Approach to Land Use Sustainability) SALUS is a process-based crop model developed by Texas A&M University that can simulate the growth, development, and yield of various vegetable crops. It is used for research and decision support in sustainable vegetable production systems. AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 29

30. Examples of Different CSMs in Different Vegetables Vegetable name Model Name Purpose Tomato TomGro Simulates tomato growth and development under varying environmental conditions to predict phenological stages, biomass accumulation, and yield for crop management decision-making Potato SUBSTOR Part of the Decision Support System for Agrotechnology Transfer ( DSSAT ); simulates potato growth, development, and yield under different soil and management conditions to optimize potato production Cabbage CABGROW Specifically designed for simulating cabbage growth and yield ; predicts leaf area expansion, biomass accumulation, and yield formation to optimize cabbage production Carrot CARMO Simulates carrot growth and yield based on environmental factors, soil characteristics, and management practices ; predicts root growth, biomass accumulation, and root quality to optimize carrot production . AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 30

31. Vegetable name Model Name Purpose Lettuce LETTUCE Simulates lettuce growth and yield under varying environmental conditions; predicts leaf expansion, biomass accumulation, and harvestable yield for decision-making . Pepper PEPGRO Simulates pepper fruit growth under different environmental conditions; predicts fruit size, biomass accumulation, and yield to optimize pepper production. Sweet Corn Hybrid-Maize Adapted for simulating sweet corn growth and yield; predicts sweet corn growth, development, and yield under different environmental and management conditions. Cucumber CUCUM Developed for simulating cucumber growth and yield; predicts vine growth, fruit development, and yield formation under varying environmental and management conditions. AGRICULTURE UNIVERSITY, JODHPUR Examples of Different CSMs in Different Vegetables 15-10-2025 Vegetable Science 31

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33. AGRICULTURE UNIVERSITY, JODHPUR Case Study 1 The objectives of this work were, to evaluate the performance of DSSAT-CROPGRO-Tomato in predicting tomato yield under different thermal environment . IARI, New Delhi Sunil et. al. 2008 15-10-2025 Vegetable Science 33

34. AGRICULTURE UNIVERSITY, JODHPUR Sunil et. al. 2008 IARI, New Delhi SIMULATING THE EFFECT OF THERMAL ENVIRONMENT ON TOMATO WITHCROPGRO (DSSAT V4) MODEL 15-10-2025 Vegetable Science 34

35. Fig.1. it is very clear that simulated yields and observed yields are sufficiently close so that it can be used for predicting the yield of tomato. For further evaluation of the model, yield data of the same variety of tomato obtained from a study conducted by Pachauri et al . (1986) at Division of Vegetable Crops, Indian Agricultural Research Institute, New Delhi was used. It is clear from the Figures (2 and 3) that DSSAT was model able to predict both biomass and LAI more precisely. The DSSAT model is able to predict LAI and biomass with sufficient accuracy. The model correctly predicted biomass, leaf area index (LAI) and total yield, So the model should be a useful tool evaluating the potential yield of tomato under various thermal environments. AGRICULTURE UNIVERSITY, JODHPUR Results Sunil et. al. 2008 IARI, New Delhi 15-10-2025 Vegetable Science 35

36. The objectives of this work were, for the VegSyst model, to (a) revise and adapt the calibration for tomato, pepper and melon to simulate seasonal dry matter production (DMP), critical N uptake and ETc based on the Almeria Radiation method, and (b) validate the model to simulate DMP, critical N uptake and ETc in several crops grown in greenhouses in SE Spain. An example of the use of the VegSyst ‐DSS in tomato will be presented. . Gallardo , et al ., 2018 Case Study-2 15-10-2025 Vegetable Science AGRICULTURE UNIVERSITY, JODHPUR Almeria, Spain 36 15-10-2025

37. Gallardo, et al ., 2018 Almeria, Spain AGRICULTURE UNIVERSITY, JODHPUR SIMULATING THE EFFECT OF THERMAL ENVIRONMENT ON TOMATO WITHCROPGRO (DSSAT V4) MODEL 15-10-2025 Vegetable Science 37

38. Fig.1. Relationship between simulated and measured values of dry matter production (DMP) (a, b) crop N uptake (c, d) and evapotranspiration ( ETc ) (e, f) for tomato crops grown in soil (a, c, e) and in substrate (b, d, f). The solid line corresponds to the 1:1 linear relationship. Gallardo, et al ., 2018 AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 38

39. VegSyst accurately simulated daily dry matter production (DMP), N uptake and ETc Simulated values of DMP were close to the 1:1 line indicating good performance of the model for winter/spring crops grown in soil and in substrate The statistical indices confirmed the good performance of the VegSyst model for simulating accumulated DMP in tomato for crops grown in soil The use of the VegSyst DSS in combination with sensor techniques for irrigation and N management will permit the implementation of prescriptive-corrective management of both irrigation and N in greenhouse-grown vegetable crops in SE Spain and similar environments . Gallardo, et al ., 2018 AGRICULTURE UNIVERSITY, JODHPUR Results 15-10-2025 Vegetable Science 39

40. In this study, we proposed using the cropping system model SUBSTOR-Potato model was used to simulate the baseline and future potato growth and development. Results indicated that SUBSTOR-Potato had an appropriate accuracy to simulate climatic and growth parameters of potato. Simulated results showed that the maximum leaf area index (LAI), days to tuber initiation (DTTI), days to harvest (DTH) and fresh tuber yield of evaluated variety will be declined as affected by future climate change. Case Study- 3 AGRICULTURE UNIVERSITY, JODHPUR Vegetable Science 40 Adavi et al ., 2018 IRAN 15-10-2025

41. AGRICULTURE UNIVERSITY, JODHPUR Results Comparison of simulated and observed fresh tuber yield, leaf area index (LAI), day to tuber initiation (DTTI) and day to harvest (DTH). The coefficient of determination (R2) between the simulated and observed tuber yield was 0.97 and the values of this parameter were 0.79, 0.86 and 0.94 for LAI, DTTI and DTH, respectively with a slope of the regression equation that was not statistically different from one. The SUBSTOR-potato model was shown to be suitable to simulate tuber growth and yields over a wide range of current growing conditions and crop management practices across many geographic regions. 15-10-2025 Vegetable Science 41

42. In this study, The selection of high yielding, optimization of water levels, sowing dates to improve potato crop production under current and future climate change have to be evaluated. The DSSAT family of models was used extensively to simulate potato growth and yield . Abdrabbo , et al ., 2010 Case Study-4 AGRICULTURE UNIVERSITY, JODHPUR Vegetable Science 42 Journal of Applied Sciences Research , 6(6): 751-755, 2010 15-10-2025

43. AGRICULTURE UNIVERSITY, JODHPUR Results Table:1 Comparison between observed and predicted data for tuber yield (kg/ha) at El- Beheira Abdrabbo , et al ., 2010 Egypt 15-10-2025 Vegetable Science 43

44. AGRICULTURE UNIVERSITY, JODHPUR Results Fig :1 Comparison between observed and predicted data for potato yield at difference irrigation levels during twoseasons of 2005/2006 and 2006/2007 Abdrabbo , et al ., 2010 15-10-2025 Vegetable Science 44

45. AGRICULTURE UNIVERSITY, JODHPUR Results T he output data predicted from the SUBSTOR Potato model were in harmony with the observed data for tuber yield. Regarding the effect of different irrigation treatment, data showed that using 100 % irrigation level increased potato tuber yield as compared to other irrigation levels. The same curve was obtained from predicted model, difference in tuber yield due to irrigation water levels in both results from observed and predicted data, 100 % irrigation water gave the highest value for two cultivars yield compared to the other irrigation levels (25111, 26088 kg/ha) and (25092, 25122 kg/ha) for observed data in the first and second season, respectively, and (25579, 26550 kg/ha) and (25582, 25590 kg/ha) for predicted data in the first and second season, respectively. The Valour cultivar gave the highest potato yield (Kg/ha) compared with Dezareah cultivar during the two seasons of 2005/2006 and 2006/2007. Abdrabbo , et al ., 2010 15-10-2025 Vegetable Science 45

46. AGRICULTURE UNIVERSITY, JODHPUR Results Table 2:- Comparison between observed and predicted data for tuber yield (kg/ha) Abdrabbo , et al ., 2010 Egypt Potato yield at irrigation level 100% for the two cultivars ( Dezareah and Valour ) gave the highest value under climate change for two GCM Model compared to the other irrigation level T he mean potato yield will decrease from 11 to 13 % under climate change during year 2050 for A1 scenario. 15-10-2025 Vegetable Science 46

47. AGRICULTURE UNIVERSITY, JODHPUR Results Fig. 2: Comparison between current and future potato production (kg/ha) by using two general circulation models (HadCM3 and CSIRO) for A1 Scenario during 2050 Abdrabbo , et al ., 2010 Egypt 15-10-2025 Vegetable Science 47

48. AGRICULTURE UNIVERSITY, JODHPUR Results Abdrabbo , et al ., 2010 Egypt Data revealed that potato yield for the two cultivars under climate change conditions will be decrease under the two models. Potato yield for CSIRO model decreased than HadCM3. The potential impact of climate change on potato yield was evaluated by simulating two cultivar and irrigation requirements level on simulated potato production with climate change output models (CSIRO, and HadCM3) for A1 greenhouse gases Scenario by year 2050 compared with that predicted under the current condition 2005/2006 15-10-2025 Vegetable Science 48

49. In this study, we proposed using the cropping system model ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) that is capable of providing information on water requirements to grow vegetables. Kim, et al ., 2020 Case Study-5 AGRICULTURE UNIVERSITY, JODHPUR Vegetable Science 49 Source:Agronomy 2020, 10, 1525; doi:10.3390/agronomy10101525 15-10-2025

50. Table 1. Measured and ALMANAC-simulated marketable yields of 8 vegetables grown in Temple, TX ALMANAC simulated marketable yields of all the vegetables, except spearmint, agreed well with the measured yields (Table 1 and Figure 1). As shown in Figure 1. ALMANAC’s simulated yield of spearmint underestimated the measured yield by 2.5 Mg ha−1 (Table 1) AGRICULTURE UNIVERSITY, JODHPUR Results Kim, et al ., 2020 15-10-2025 Vegetable Science 50

51. Table 2. Measured moisture contents, ALMANAC-simulated Water Use Efficiency (WUE) for either wet or dry yield basis, and measured WUE reported in the literature As shown in the results, ALMANAC accurately simulated yields of all eight vegetables. Although a limited number of years of data was used to develop each crop model, the model also could realistically simulate WUE when we compared the simulated WUE values with references. Thus, we assumed that ALMANAC model has been successfully validated. Following the successful validation of the ALMANAC model, we applied the model to predict vegetable production in the Winter Garden Region, and simulation results were used for production economic analysis AGRICULTURE UNIVERSITY, JODHPUR Results Kim, et al ., 2020 Source:Agronomy 2020, 10, 1525; doi:10.3390/agronomy10101525 15-10-2025 Vegetable Science 51

52. Crop models have the potential to transcend our understanding of how crops interact with agronomic management across space and time. Models have been accepted as useful tools that help agronomists, farmers, policy makers, and other researchers make more informed decisions and recommendations. In this paper, we provide a guide on how to build a process-based crop model within a larger cropping system framework. Pasley, et al ., 2023 Case Study-6 AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 52

53. Fig. 1. Conceptual figure of the relationship between model complexity and uncertainty Adapted from Passioura 1996 and Gaber et al. 2009). AGRICULTURE UNIVERSITY, JODHPUR How to Build a Crop Model : A Review Pasley, et al ., 2023 15-10-2025 Vegetable Science 53

54. Limitations AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 54

55. Future Prospects Integration of Remote Sensing Data Enhanced Spatial Modeling Integration of Machine Learning and Artificial Intelligence Climate Change Adaptation Improved Crop Genotype Models Dynamic Pest and Disease Models Enhanced Decision Support Systems Open Data and Model Interoperability AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 55

56. Crop Simulation Models (CSMs) play a vital role in vegetable production by providing researchers, and growers with valuable insights into crop growth, development, and yield under varying environmental conditions and management practices. Through the integration of weather data, soil characteristics, crop genetics, and management inputs, CSMs enable informed decision-making for optimizing irrigation scheduling, fertilizer management, and pest and disease control. Models such as DSSAT, VegSyst , STICS, CropGro -Garden, ALMANAC, APSIM and SALUS offer valuable tools for assessing the impact of interventions on vegetable productivity, food security, and environmental sustainability. As vegetable production faces increasing challenges from climate variability, resource constraints, and changing consumer demands, CSMs will continue to be indispensable tools for enhancing productivity, resilience, and profitability in vegetable farming. CONCLUSION AGRICULTURE UNIVERSITY, JODHPUR 15-10-2025 Vegetable Science 56

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