YOUR BEST PRACTICE
Choosing the right fertigation decision support system for your operation
By Jackson Stansell I
ncreasing margins, managing farm labor challenges and meeting the increasing demands for excellent environmental stewardship are problems that all
farms face. These issues are particularly challenging on irrigated farms requiring high intensity management. Properly managing nutrients is an important aspect of all these challenges. For farms with irrigation, fertigation is often part of a good nutrient management plan. According to the U.S. Department of Agriculture, nearly 12 million acres were fertigated in the U.S. in 2018. Of these acres, nearly 3.5 million were in corn production. Orchards and vineyards and vegetable production represented 2.6 and 1.2 million acres, respectively. Other heavily fertigated crops included alfalfa, cotton, wheat, hay and potatoes.
While management practices vary between crops, three primary fertigation management strategies have been the status quo. For many farmers and agronomists, experience and intuition built through years of observation and trials are the primary factors behind fertigation management plans and adjustments. For others, guidance from university extension services is paramount to their management practices. In recent years, plant tissue analysis has also emerged to track plant nutrition throughout the season whether to adjust the plan during the season or prepare a better plan for the next season. While each of these management strategies has merit, they also present challenges.
28 Irrigation TODAY | Spring 2024
Particularly, they tend to lack field-specific, year-specific and crop-specific calibration. They also tend to be low-resolution (one data point or decision for the whole field). Coupled with the effects of temporal (season-to-season and within season) and spatial variability in crop nutrient demand, these approaches often fall short of optimal. Either more nitrogen is applied than the crop demanded leaving risk of environmental nitrogen contamination or too little nitrogen is applied to meet crop demand for maximum productivity. Several decision support approaches are available to support farmers and agronomists in making optimal fertigation management decisions. In general, these decision support systems fall into three categories: advanced plant sap analysis, crop and soil models, and sensor- based algorithms.
Plant sap analysis is a step up from standard plant tissue analysis. It involves high- frequency (about weekly) plant-tissue sampling from old and new leaves on the plant. Samples are sent to the lab, analyzed, returned and mapped to the field or sampling location. From there, the analysis must be translated to a fertigation decision. Success with PSA requires proper sample handling, quick turnaround times, consistent timing (time of day, specifically) and identical lab selection. Some of the biggest benefits are that it provides multinutrient information, is not dependent on weather conditions and requires little to no data to get started. Some limitations of PSA are that it is labor intensive,
is not calibrated to hybrid/variety or soil type, and generally provides only one data point for a large area.
Crop and soil models integrate weather, crop hybrid/variety, soil property and production practice information to predict total crop nutrient usage and remaining crop nutrient demand. Using a crop and soil model requires input of significant data including field location, application history, soil sample data, crop planting or growth data, and other production practice information. These systems also often require the user to enter a yield or production goal. Once this information is entered, the user can run the model to determine crop nutrient use up to the current date. Some of the benefits of crop and soil models for fertigation decision support are that these models are not impeded by environmental conditions or connectivity, users can generate recommendations on demand, insights for multiple nutrients may be provided, and results may be used for planning and scenario analysis. Some of the limitations of crop and soil models for fertigation decision support include no in-season calibration; extensive recalibration by region, crop and soil; and significant user data input requirements.
Sensor-based algorithms use real-time and accumulated data from images, soil sensors and other instruments to quantify crop nutrient demand and changes in soil nutrient availability. In some cases, sensor- based algorithms combine real-time and
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