This year CSANR sponsored registration for several WSU students to attend Tilth Producers of WA annual conference. We will be posting reflections written by the students over the next several weeks. Please feel free to comment and give these students your feedback.
In humans, after decades of research and innovation, it is still very tricky for medical practitioners to accurately predict a child’s delivery date. Though the doctors estimate delivery dates for expectant mothers, these dates are rarely exactly correct, despite the level of technology involved.
Predicting crop harvest time is not all that different from estimating due dates. Have you ever wondered how complex and challenging it could be to predict the precise harvest date of crops, especially for small scale farmers with limited resources to invest in specialized technologies to support on-farm decision making system? As with doctors and delivery due dates, farmers have an estimate of the time required for the plants to achieve some phenotypic attributes like flowering, fruit setting and ripening etc., but getting it exactly right is rare. This prediction is usually based on prior knowledge about the crop’s biology. However, plant growth and development is largely dependent upon elements of the immediate environment (temperature, light duration, humidity etc.). Thus, crop output in terms of quantity, quality, and timing is dependent on the micro-environment. Therefore for a typical small scale organic farmer, a big question is how to accurately and precisely predict the time period in which harvest is optimal. This is critical to meeting the volatile demand of customers in a timely way (CSA, food co-ops etc.).
The onset of climate change has inevitably resulted in extreme and unpredictable weather (drought, flooding, wildfires etc.) which has led to unexpected responses in crop performance. Now more than ever it is difficult for farmer to know the likely output and harvest time beforehand. Despite the numerous changes attributable to climate change, one thing that is constant and certain is that farmers have to keep up with the demand of their customers, in timely manner, if they want to remain in business. Knowing the crops and varieties to plant, at what time and when to expect harvest is crucial to achieving this goal.
A group of researchers from Oregon State University under the Small Farms program is taking the bull by the horns. They have developed a simple and affordable tool to help farmers make better decisions and plan amid the stochastic weather situation. CROPTIME is an online tool which uses a combination of site-specific local environmental factors to help farmers make accurate and precise predictions of the growth and harvest times for their vegetables. This tool stands out from other similar tools in that it uses weather data from local areas in the Pacific Northwest region of Washington and Oregon, and is therefore able to give very accurate output that farmers in this area can rely on.
The CROPTIME tool is based on the degree day model to help farmers make decisions. Degree day models have been intensely used to predict the emergence, growth and development for insect pests, consequently helping farmers and pest managers to properly time the application of insecticides, baits and traps to reduce losses and damage caused by insects and other invertebrate pests. Degree day (DD) modelling for pests has saved money and reduced excess pesticide use. A degree day is calculated by subtracting the minimum developmental temperature for a particular ectothermal organism from the daily mean temperature. Similar to insect degree day models, CROPTIME currently has: over 70 DD models to predict harvest dates for vegetables varieties, 6 weeds DD models for proper weed control and a DD nitrogen tool for optimal nitrogen application crops.
Farmers that intend to use CROPTIME select a very proximate weather station (over 1500 weather stations are available) and choose a model that best meets their farm setting. A proposed planting date is entered and the model is run to produce the crop time schedule based on the prevailing data available for the site of interest. The tool is very flexible for users, and can be customized in different situations for a variety of outputs.
As more and more farming decision-support systems are being introduced to help farmers, there will be a need to integrate these tools into a central piece that can be easily managed and is accessible to farmers and other end users. New advances in research and knowledge will also need to be included in these tools so they don’t become obsolete. The design of the tools should involve all the stakeholders and policy makers. Furthermore, as more varieties of crops and vegetables are developed and released for farming, they should be incorporated into the decision support tools. Most importantly, extension agents who serve small acreage farmers will need to intensify their efforts in teaching the usage of these kinds of tools.
While predicting baby delivery dates is outside my area of study, I see great promise for farmers in more accurately predicting optimal harvest dates, and CROPTIME is a tool to help.