Article by Richard Meinert
In the simplest form a Nutrient Management Plan is an inventory of the nutrients produced on the farm or needed by crops that are, or will be, produced, and a list of planned applications needed to distribute those nutrients to individual crop fields to support the growth of the desired crop, for all fields on the farm. Historically these plans were pretty simple. A farm would apply manure by spreading it on the fields until they ran out, then they would apply fertilizer where they thought they would need it with little regard for how an individual application would affect the field, the crop or the environment. Today fertilizer is too expensive to waste and excess nutrients in a field are more likely to run off to contaminate ground or surface water. The goal of the Extension Nutrient Management Planning Program is to help famers target their nutrients to the portions of the fields that need them.
The key to accomplish this is knowing what is there already. Remote sensing technology is the tool that can provide that information to farmers for each individual field at a cost they can afford. UConn Extension’s Nutrient Management Planning team is using this technology (aircraft mounted camera-like sensors) to help farmers use manure and fertilizer more effectively. Eleven farms across Connecticut are cooperating in this project to show farmers how remotely sensed imagery could be used to guide future manure and fertilizer applications. Farms agreed to allow UConn faculty access to 35 fields to take soil and crop samples and to allow their fields to be photographed during the growing season. Farms receive copies of all of the sample results during the growing season to make management decisions. During the winter farms come together as a group to see the imagery, discuss the results for their fields and to plan the next year’s manure and/or fertilizer applications using the analysis results and imagery to guide their decisions.
The photo above is an example of the aerial imagery used in this process, in this case an NDVI image. NDVI stands for Normalized Difference Vegetative Index. NDVI was originally developed to determine land cover differences in vegetation from space. However by bringing the sensors closer to earth and targeting individual crop fields the technology can pinpoint areas in the field that are stressed and likely to yield less crop. NDVI basically calculates a ratio of the amount of light reflected in various wavelengths. This ratio number is the mathematical value of the “greenness” of the plant. Darker green color is indicative of healthier plants. This ratio is calculated for each pixel present in the images, as shown by the enlarged section of the photo. Each pixel or square visible in the enlarged section represents a 50 X 50 cm (19.6 X 19.6 inch) potion of the field surface. The resulting values are then color coded into ranges so the well fertilized healthy vegetation in the field appears as dark green, the less well fertilized or less healthy regions vary from light green through yellow and the worst vegetation in the field shows as orange. Areas with little or no vegetation appear red. This color-coding makes it easy for the farmer to understand where the best areas of the field are located.
Capturing the imagery and calculating the NDVI is the easy part. Commercial companies provide imagery for millions of acres of farmland across North America each year. The challenging part of this project is answering the question, “So now what?” This is where Extension is focusing its attention. There are 4 labelled locations in the field image. These are the points in the field chosen by Extension faculty to represent the poor, better and best regions in the field. Using hand held GPS devices faculty and students visit each location and mark out a 5 X 10 foot region for detailed sampling and data collection. Plant population is counted, soil samples are taken, and plants are harvested, weighed, ground and analyzed for dry matter and nutrient content.
When all of the laboratory work, and other data is collected and collated we calculate the overall yield information for the various colored regions in each field. Since we have data on the yield and the soil we can make recommendations that give farmers a more accurate estimate of the nutrients that should be applied to the various regions of the field. Having identified areas of the field that don’t need fertilizer as well as those areas that may need more nutrients the farmer can better target the areas that need additional fertilizer and save on areas that need less. Some farms use the information to maximize production per acre so they can farm fewer acres. The point is that having accurate information allows each farm to manage the field in a way that best fits their need without guessing and without over applying nutrients and having them be lost and possibly cause pollution.
Currently this program is effective, but not affordable without grant funds from off-farm sources. There is insufficient demand from farmers in New England, so the cost for imagery is too high for an individual farm to justify. The grant project is paying to obtain the imagery, and introduce the technology to the farms. UConn Extension’s work allows us to understand the various costs and obstacles involved in adapting this process to New England farms, which tend to be much smaller and more widely scattered than Midwest farms. The team has purchased a drone and is working on programming hardware and training a pilot to fly the drone and turn photos into usable images. There is a significant amount of computer processing of imagery needed to create a field map usable for nutrient applications. This will be a large portion of the effort of the team for the 2017 crop season.