|
|
|
Project Details:
Yield mapping has called the attention of growers and researchers by demonstrating the degree of yield variability in areas much smaller than whole fields. Although growers recognize that yield varies in their fields, it is difficult to measure and map that variability without a yield monitor. Grain crops, soybean and cotton have led the way when it comes to yield monitoring technology. While yield monitoring has been practiced for approximately 20 years in these commodities, it is not widely used, or is still under development, in other crops like sugarcane, tomatoes, peanuts, sugar beets, pistachios, citrus and coffee. In other crops like alfalfa the technology has not even existed until recently.
Growers who use site-specific management technologies consider a yield map a valuable tool to support crop and soil management decisions. They can follow up by scouting and investigating why yield variations occur and begin to establish relationships between yield variability and yield limiting factors such as soil fertility, soil physical impediments (drainage), salinity, weeds, pests, nematodes, equipment malfunction, etc. By targeting resources in the right place and at the right amount, growers can achieve environmentally sound practices and maximize economic returns by increasing crop yield and optimizing resources. Profitability maps can also be created based on gross returns calculated from yield and associated production costs data. With this information, growers know specifically where on their field profits and losses come from to support management decisions. For example, in areas of low or no economical return, a grower can apply only enough fertilizer to break even or not even fertilize those areas. Additionally, yield monitors can enhance on-farm research (such as comparing variety performance, seeding and fertilizer application rates, pesticide types and rates, etc.) by facilitating yield assessment.
Particularly for nutrient management, yield monitoring data could be explored in different ways. Nutrient management practices in alfalfa fields in California should be based on yield potential and crop removal, which can vary two to threefold throughout the field. Using yield maps, we can create fertilizer recommendation maps that account for soil fertility spatial variability and at the same time account for spatial variability in yield potential, considering for differences in the amount of nutrients removed by alfalfa throughout the field. Hence, fertilizer recommendations will depend on soil fertility level as well as the yield potential for different parts of the field.
One could also focus soil and plant tissue sampling on problematic areas of the fields in order to determine if soil fertility is limiting yield. And, if low growth areas are not associated with soil fertility, application rates could be reduced in those areas to optimize fertilizer use. Similarly, high yielding zones could be sampled to compare with lower yielding areas to determine if the cause is related to soil fertility or some other factor. By using the bale sampling tissue testing method and noting sampling locations with a GPS, hay quality and crop nutrition status could be linked with yield data for specific locations in the field. One could also explore nutrient variability interaction with yield, since optimum levels of nutrients vary according to other nutrient’s availability and yield. Additionally, crop response to local soil properties of on-farm experiments involving strips of varying fertilizer rates can be easily assessed with yield monitoring. Overall, we expect that the use of yield monitoring data can significantly increase fertilizer use efficiency by meeting specific crop needs and reduce nutrient (mainly P and K) losses to the environment.
We propose to evaluate the performance of a newly-developed alfalfa yield monitor created for small square balers, which to our knowledge has not been evaluated in field research trials. The accuracy of these devices depends on appropriate installation, calibration, and operation. We will evaluate the system’s accuracy by measuring plant height, harvesting and weighting control plots/areas of 3x20ft throughout the field with a cycle bar mower for approximately five to six cuts in 2011. Additionally, in association with ongoing research from Nebraska State University, we will collect crop canopy sensing data and assess specific vegetation index relationships to alfalfa yield.
In a second stage/year we expect to explore the benefits of associating yield monitoring data to nutrient management and establish control plots to assess on-farm yield responses to different nutrient management treatments.
|
|
