Project Details:

Title:
Breaking Barriers: Developing Tools for Moving Kansas Irrigated Soybeans Beyond 70

Parent Project: This is the first year of this project.
Checkoff Organization:Kansas Soybean Commission
Categories:Education
Organization Project Code:1677
Project Year:2016
Lead Principal Investigator:A Ray Asebedo (Kansas State University)
Co-Principal Investigators:
Keywords:

Contributing Organizations

Funding Institutions

Information and Results

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Project Summary

Soybeans are an important source of income for Kansas farmers. With higher market value and less required inputs compared to other crops, soybeans continue to provide higher profit and economic stability to the Kansas farmer. It is commonly understood by farmers that one of the most yield limiting factors for soybeans in Kansas is precipitation. Therefore, irrigation should make higher yield levels attainable, by overcoming the limited available water scenario. However, with observed yield levels commonly between 50 and 70 bushel per acre, expectations by the Kansas soybean farmer have not been met even under sufficient irrigation. This requires us to ask “If water is not limiting our soybean yield, then what is?”. Next to water, N is the most commonly recognized yield limiting factor in crop production. Because of the symbiotic relationship with rhizobium, supplying the soybean plant with N through N2 fixation, N management of soybeans is only relevant if potential yield levels have increased the N requirement beyond what the rhizobium can supply. However, assessing yield potential inseason in a manner that is both accurate and time efficient is not feasible using traditional “counting pod” methods. In addition, farmers lack tools that can assess rhizobium N fixation efficiency. Therefore, it is very difficult for farmers to assess the in-season yield potential of soybeans and determine if the rhizobium is supplying enough N to meet crop demand. Thus potential high yield levels and more profit per acre is lost because the lack of information necessary to assist farmers in intensively managing irrigated soybeans for high yield. sUAS and mobile technology such as iPhones and iPads can be integrated together for the development of soybean scouting systems to provide Kansas soybean farmers with time efficient decision support tools to manage each field to its maximum profitable yield level

Project Objectives

Project Deliverables

A preliminary set of experiments will be carried out using pots in the greenhouse to determine the efficiency of a diverse rhizobial strains (including the currently used strain as a control) by growing standard and high oleic soybean varieties in soils obtained from all three locations targeted in this study. Temporal (both on a weekly basis and at different time points in a day) xylem sap and stem segments will be obtained, and ureide, nitrate and amino acids will be determined calorimetrically, to (i) identify the most efficient rhizobial strain for future field study, (ii) determine the interval, i.e. number of days between sampling and the right time of the day for the stem sap and stem segments to be collected. Ureide estimation will be used to determine percentage of the total N fixed from the atmosphere (%Ndfa). Robustness check for the ureide estimation will be carried out by collecting information on number, mass and size of the nodules formed and correlated with ureide estimation, pod yield and total biomass.

Field studies will be established at three irrigated locations with a history of high yield in collaboration with KSU experiment fields and Kansas farmers. The four treatment factors will consist of seed inoculant, varieties, late-season N, and irrigation. Seed inoculants will consist of two different seed rhizobial inoculations: a currently used strain and a promising strain from greenhouse studies from Year 1. Variety treatments will consist of two varieties: standard and high oleic. Late-season N treatments will consist of three N rates, 0, 30, and 60 lb N/ac applied at beginning pod (R3). Irrigation treatments will consist of two irrigation scheduling based on percentage of crop evapotranspiration (ET) rate: 50%, and 100%. Prior to planting, soils will be characterized for yield limiting soil variability pertaining to electrical conductivity, organic matter, and pH using on-the-go soil sensing systems. Baseline soil fertility will be measured by obtaining physical soil samples at 0-6 inch and 6-24 inch depths and will be submitted to the KSU soil testing lab to be analyzed for Mehlich-3 P, K, Zn, Cl, NH4-N, NO3-N, and SO4-S. Soil fertility will be assessed and fertilized for a grain yield goal of 90 bushel per acre.

Soybean physical response to environmental interactions will be captured and quantified on a weekly basis with remote sensing and physiological measurement techniques. Remote sensing techniques will include aerial multispectral and thermal imagery from sUAS. Physiological measurements including photosynthesis, stomatal conductance using LICOR XT 6400, photochemical efficiency using fluorometer (OS30p+; OptiSciences), chlorophyll index using SPAD meter will be followed temporally on a weekly interval to complement the aerial imagery efforts. Ureide estimation will be used for in-field estimation of N fixed by rhizobium.
Information generated in the greenhouse experiments will support field experiments as the highly efficient rhizobial strain can be tested under field conditions during the second and third year of the project. The role of additional N applied and stored N becomes critical for pod filling, hence leaf samples will be obtained on weekly intervals from R2 stage to determine the proportion of chlorophyll a, b and carotenoids concentration and to evaluate changes in their ratios across treatment factors. Due to the labor intensive nature of physiological measurements, few agricultural remote sensing databases contain this critical information, which is essential for developing farmer tools for assessing soybean health and performance.

At physiological maturity, plots will be harvested with a plot combine and yield adjusted for grain moisture content. Grain samples from each plot will be submitted to the KSU soil testing lab and analyzed for nitrogen, oil, and protein content. Weather data relating to ambient air temperature, wind speed, relative humidity, and precipitation will be collected from automated weather stations that will be placed within the study area. Vegetation indexes calculated from the multispectral imagery and plant canopy temperature by thermal cameras will be used for data fusion with soil, weather, physiological measurements, and grain yield data for multivariate, spatial, and time series analysis for determining yield limiting factors and treatment effects. Data collected on year 1 will be used to build the prototype sUAS crop scouting tool that can be used with Apple iPhone and iPad. This sUAS crop scouting tool will be validated and will be further developed over the course of years 2 and 3.

Progress of Work

Final Project Results

Benefit to Soybean Farmers

Performance Metrics

Project Years