Crop Modeling Definition, Use Cases and Advantages
As the world population continues to rise, food production will have to be increased to meet and sustain the demands of our rapidly growing population. Not only will food production need to increase, but yields will need to be able to withstand climate changes which include increased temperatures and decreased rainfall patterns.
By understanding and being able to predict crop production outcomes under various climatic situations and management approaches, farmers will be better equipped with adaptation strategies to maximize crop growth as sustainably as possible. Crop modeling tools offer a way to evaluate trade-offs of potential adaptations in climate and can help form the basis of decision-support systems for farmers.
What is Crop Modeling?
Crop modeling in agriculture uses quantitative measurements of ecophysiological processes to predict plant growth and development based on environmental conditions and crop management inputs.
These models simulate a crop’s response (growth or yield, for example) to the environment, management, water, weather, and soil parameters, as they interact over the course of a growing season. These tools mimic the growth and development of crops to mathematically represent the various components within the cropping system.
The concept of crop modeling dates back to the 1960s when researchers modeled agricultural systems by combining both physical and biological principles. Crop models rely on measurable inputs (by sensors, machines, or hand measurement) to determine whatever output is of interest (plant growth, crop yield, soil nitrogen, crop staging, etc.).
Data Needed for Crop Modeling
Crop modeling tools require a minimum set of data as inputs in order for the model to accurately complete a prediction. Some types and examples of input include management (planting date, planting density, crop variety, fertilization, and irrigation), soil (drainage class, pH, organic matter content, and sand/silt/clay content), physiology (leaf area index, total biomass above ground, plant height, and stand count), and climate (rainfall, air temperature, wind speed, photoperiod).
In order to create a crop model, a variety of the inputs mentioned above need to be collected as well as the measurement of the factor that you’re wanting to predict. This then allows you to calibrate your model after determining what factors were the most influential or important in the model.
Then the model needs to be validated with an independent data set to determine its accuracy and fine-tune the number of inputs that are necessary. Typically a parsimonious model, or model with the fewest variables but greatest accuracy, is desired.
There are a variety of public and privately-used crop modeling tools focusing on specific crops or regions. Some of the limitations that currently exist with these simulators are that: there is no single program or set of model parameters that have been identified for all global regions and crops, there’s limited precision to quantify crop responses to micronutrient stress, and there is a limited amount of validation data available to improve models across crops.
Crop modeling in agriculture has more recently been a key tool in assessing the impact of future climate change. By collecting large amounts of input data for model calibration and validation, researchers can evaluate possible adaptation strategies and management decisions under varied climate conditions.
Benefits of Crop Modeling
There are numerous benefits to crop modeling, such as increased efficiency, increased yields, and lower environmental footprint.
Crop yield modeling can help drive efficiency in agricultural production systems by allowing farmers to manage their inputs more efficiently. These models work to achieve maximum crop performance while minimizing inputs, such as fertilizers, irrigation, or other applications. This allows farmers to focus on management practices that best serve their production systems.
One of the main goals in precision agriculture is to achieve the maximum crop yield while minimizing inputs and losses from cropping systems. By understanding what inputs are most important to increase yield, farmers can identify the key timepoints for management practices to optimize their yield and ROI.
Lower Environmental Footprint
Crop yield prediction can help lower the environmental footprint by demonstrating the benefit of specific cropping practices (such as crop rotation or organic fertilizer application) to positively increase yield and lower carbon footprint. Similar to efficiency, by managing and minimizing the number of inputs, there is a reduction in unnecessary or excess application of fertilizers or pesticides that can negatively impact the environment.
Crop Modeling with Agmatix
Agmatix’s technology facilitates predictive agronomic modeling using automatic data ingestion and statistical analysis software. With standardized and analyzed data, machine learning-based models assist with crop modeling for a variety of crops.
While crop yield prediction models will differ across crops and regions, there is usually a core set of model inputs that are needed once the modeling objective has been decided.
Our crop yield modeling capabilities use multiple data sources across a wide range of production environments and nutrient parameters. Once a model is developed, it is possible to inversely map management decisions and nutrient inputs that lead to optimal yield in a production region.
The Future of Agronomics Using Crop Modeling
Crop modeling in agriculture has the potential to provide valuable insights and solutions for ag professionals. With improved agronomic data collection, predictive modeling using multiple datasets will allow researchers and farmers to better understand the parameters and management practices that are most influential on crop growth.
Being able to explore potential outcomes over time, given changes in climate or other inputs, opens up a whole new perspective as we work to improve efficiency and reduce environmental footprints.