In the intricate and interconnected world of food, land, and water systems, the role of crop growth models cannot be overstated. These models serve as essential tools in foresight analysis, helping us navigate the complexities of agricultural transformation in a rapidly changing environment.

Imagine standing at the edge of a vast field, looking out over the crops that sustain communities around the world. Now, picture the myriad of factors that influence these crops: shifting climates, evolving agricultural practices, and the constant demand for more efficient resource use. Crop growth models bring clarity to this intricate web, allowing us to predict future scenarios with a remarkable degree of accuracy.

As we grapple with the realities of climate change, these models become even more critical. They enable us to simulate how rising temperatures, changing precipitation patterns, and increased CO2 levels will impact crop growth. This foresight is invaluable, guiding us to develop strategies that mitigate the adverse effects of climate change and bolster the resilience of our food systems.

Moreover, crop growth models offer a lens through which we can evaluate different agricultural practices. By simulating various methods, such as irrigation techniques, fertilization regimes, and pest management strategies, we can identify the most effective approaches for enhancing yields sustainably. This not only helps in boosting productivity but also ensures that our agricultural practices do not harm the environment.

Resource optimization is another area where crop growth models shine. With water and nutrients becoming increasingly scarce, these models help us use these resources judiciously. By predicting the outcomes of different management strategies, they enable us to create more sustainable and efficient food production systems.

For policymakers and stakeholders, the data provided by crop growth models is indispensable. These models offer quantitative insights that inform policy development, investment decisions, and regulatory frameworks, all aimed at enhancing food security and environmental sustainability.

Ensuring long-term food security is perhaps the most pressing challenge we face. Crop growth models help by forecasting potential threats to crop production and identifying adaptive strategies. They allow us to understand potential yield gaps and the measures needed to bridge them, ensuring that we can continue to feed a growing global population.

In the face of climate change, adaptation and mitigation strategies are crucial. Crop growth models facilitate the design of these strategies by allowing us to explore various options, such as developing climate-resilient crop varieties or adjusting planting schedules. This proactive approach is vital for safeguarding our food systems against future uncertainties.

Furthermore, by integrating crop growth models with socioeconomic data, we gain a more comprehensive understanding of food systems. This integration helps us see how changes in crop productivity might affect food prices, farmers’ livelihoods, and overall economic stability, providing a holistic view that is essential for effective decision-making.

Technological innovations in agriculture also benefit from crop growth models. By predicting the potential impact of new technologies on crop yields and resource use, these models support the adoption of innovations that enhance both productivity and sustainability.

Crop growth models help simulate various future scenarios under different conditions such as climate change, land use changes, and agricultural practices. This allows for the assessment of potential impacts on crop yields and food security, providing valuable insights for planning and policy-making.

These models can incorporate climate data to predict how changing temperature, precipitation, and CO2 levels will affect crop growth. Understanding these impacts is essential for developing strategies to mitigate negative effects and enhance resilience in food production systems.

By simulating different agricultural practices, crop growth models can identify the most effective methods for improving yields and sustainability. This includes evaluating the impact of irrigation, fertilization, crop rotation, and pest management on crop productivity and environmental health.

Efficient use of resources such as water, nutrients, and land is critical for sustainable agriculture. Crop growth models help in optimizing these resources by predicting the outcomes of different management strategies, thus contributing to more sustainable and efficient food production systems.

Policymakers and stakeholders rely on accurate and reliable data to make informed decisions. Crop growth models provide quantitative data that can support policy development, investment decisions, and the creation of regulations aimed at enhancing food security and environmental sustainability.

By forecasting potential threats to crop production and identifying adaptive strategies, crop growth models play a vital role in ensuring long-term food security. They help in understanding the potential yield gaps and the measures needed to bridge these gaps under varying future scenarios.

Crop growth models are essential tools for designing adaptation and mitigation strategies to cope with the adverse effects of climate change. They enable the exploration of various adaptation options, such as developing climate-resilient crop varieties or adjusting planting schedules.

These models can be integrated with socioeconomic data to provide a more comprehensive analysis of food systems. This helps in understanding how changes in crop productivity might affect food prices, farmers’ livelihoods, and overall economic stability.

Crop growth models aid in the development and assessment of new agricultural technologies and innovations. By predicting their potential impact on crop yields and resource use, these models support the adoption of technologies that enhance productivity and sustainability.

The Agricultural Production Systems sIMulator (APSIM) is internationally recognized as a highly advanced platform for modeling and simulation of agricultural systems. It contains a suite of modules that enable the simulation of systems for a diverse range of plant, animal, soil, climate and management interactions. APSIM is undergoing continual development, with new capability being added to APSIM Next Generation. Its development and maintenance is underpinned by rigorous science and software engineering standards. The APSIM Initiative was established in 2007 to promote the development and use of the science modules and infrastructure software of APSIM. The current members are CSIRO, The State of Queensland, The University of Queensland, AgResearch Ltd. (NZ), University of Southern Queensland, Iowa State University, (US) and Plant and Food Research (NZ)

Reference: https://doi.org/10.1016/0308-521X(94)00055-V

Website: http://www.apsim.info

AquaCrop is a crop growth model developed by FAO’s Land and Water Division to address food security and assess the effect of the environment and management on crop production. AquaCrop simulates the yield response of herbaceous crops to water and is particularly well suited to conditions in which water is a key limiting factor in crop production. AquaCrop balances accuracy, simplicity and robustness. To ensure its wide applicability, it uses only a small number of explicit parameters and mostly intuitive input variables that can be determined using simple methods.

Reference: https://doi.org/10.2134/agronj2008.0139s

Website: http://www.fao.org/aquacrop

CropSyst is a user-friendly, conceptually simple but sound multi-year multi-crop daily time step simulation model. The model has been developed to serve as an analytic tool to study the effect of cropping systems management on productivity and the environment. The model simulates the soil water budget, soil-plant nitrogen budget, crop canopy and root growth, dry matter, yield, residue and decomposition, and erosion. Management options include: cultivar selection, crop rotation (including fallow years), irrigation, nitrogen fertilization, tillage operations (over 80 options), and residue management

Reference: https://doi.org/10.1016/S1161-0301(02)00109-0

Website: http://modeling.bsyse.wsu.edu/CS_Suite_4/CropSyst/index.html

Decision Support System for Agrotechnology Transfer (DSSAT) is software application program that comprises dynamic crop growth simulation models for over 42 crops. DSSAT is supported by a range of utilities and apps for weather, soil, genetic, crop management, and observational experimental data, and includes example data sets for all crop models. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics. DSSAT has been applied to address many real-world problems and issues ranging from genetic modeling to on-farm and precision management, regional assessments of the impact of climate variability and climate change, economic and environmental sustainability, and food and nutrition security. DSSAT has been used for more than 30 years by researchers, educators, consultants, extension agents, growers, private industry, policy and decision makers, and many others in over 187 countries worldwide.

Reference: https://doi.org/10.1016/S1161-0301(02)00107-7

Website: http://dssat.net/

The Environmental Policy Integrated Climate (EPIC) model was developed to estimate soil productivity as affected by erosion and simulates approximately eighty crops with one crop growth model using unique parameter values for each crop. It can be configured for a wide range of crop rotations and other vegetative systems, tillage systems, and other management strategies.  It predicts effects of management decisions on soil, water, nutrient and pesticide movements, and their combined impact on soil loss, water quality, and crop yields for areas with homogeneous soils and management.

Reference: https://elibrary.asabe.org/abstract.asp?aid=31032 doi: 10.13031/2013.31032

Website: https://epicapex.tamu.edu/

ORYZA version 3 (ORYZA v3), or simply ORYZA, is an ecophysiological model which simulates growth and development of rice including water, C, and N balance (Bouman et al., 2001; IRRI, 2013) in lowland, upland, and aerobic rice ecosystems. It works in potential, water-limited, nitrogen-limited, and NxW-limited conditions. And it was calibrated and validated for 18 popular rice varieties in 15 locations throughout Asia.

Reference: https://doi.org/10.1016/j.agrformet.2017.02.025 https://doi.org/10.1016/j.agsy.2004.09.011

Bouman, B.A.M., Kropff, M.J., Tuong, T.P., Wopereis, M.C.S., Ten Berge, H.F.M., & Van Laar, H.H. (2001). ORYZA2000: modeling lowland rice. International Rice Research Institute, Los Baños, Philippines, and Wageningen University and Research Centre, Wageningen, Netherlands, 235 pp.

Website: https://sites.google.com/a/irri.org/oryza2000/home

The crop model known as STICS (Simulateur mulTIdisciplinaire pour les Cultures Standard, or multidisciplinary simulator for standard crops) was created at INRA, the French national institute for agricultural research, in 1996 on the initiative of Nadine Brisson with informatics support from Dominique Ripoche. The creators built the STICS model from “pieces” by bringing together the GOA (plant), BYM (water), and LIXIM (nitrogen) models, which had essentially been produced by two INRA teams, one in Avignon (with Nadine Brisson) and one in Laon (with Bruno Mary). At the beginning, STICS simulated two main crops, wheat and corn, and was used for the first time as part of the ECOSPACE project (1997) to simulate agricultural production and nitrate leaching on the basis of soil heterogeneity.

Reference: http://dx.doi.org/10.1051/agro:19980501

Website: https://www.quantitative-plant.org/model/STICS

“WOFOST (WOrld FOod STudies) is a simulation model for the quantitative analysis of the growth and
production of annual field crops. It is a mechanistic, dynamic model that explains daily crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by environmental conditions.

Reference: https://doi.org/10.1111/j.1475-2743.1989.tb00755.x

Website:https://www.wur.nl/en/research-results/research-institutes/environmental-research/facilities-tools/software-models-and-databases/wofost.htm

The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) is a partial-equilibrium economic model that simulates national and global markets of agricultural production, demand, and trade.

IMPACT is an integrated system of models that links information from climate models, crop simulation models (for example, Decision Support System for Agrotechnology Transfer (DSSAT, see above)), and water models linked to a core global, partial equilibrium, multimarket model focused on the agriculture sector. Crop models use information on the geographical distribution of crops as well as their water management (rainfed or irrigated) from the Spatial Production Allocation Model (SPAM).

Multiple information platforms currently provide fully open-access data and services from satellite Earth observations, ground observations and model outputs. The data generated by these multiple active platforms are provided following various formats and structures, which may differ considerably, depending on the objectives and applications for which they were originally conceived. Clim2Agri is a tool for extracting and sub-setting geospatial data from gridded products and model outputs for their use in crop modeling and agricultural research.