Soybean (Glycine max L.) production has grown throughout the Northeast US during the past decade propelled by the increasing demand for and price of grains worldwide and the need for a locally produced feed source for the Northeast livestock industry. In order to maintain a sustainable local supply of soybeans, there is a need for crop producers to understand management tactics to improve crop profitability and productivity, especially during times of lower grain prices such as exist today. Soil health and crop productivity are intrinsically linked, and understanding the response of crops to soil health can help agronomists and producers target management practices in the region. In addition, it can help to project long term impacts of certain management practices on soil health, offering an economic rationale for investments in more sustainable soil management practices. The purpose of this project is to evaluate and deploy a crop modeling system – CYCLES – across a set of farms in Pennsylvania with the ultimate goal of deploying a decision support system for producers.
The purpose of this project is to deploy and evaluate our approach of integrating soil health measurements with crop modeling to understand soybean yield variation across a set of farms in Pennsylvania with divergent soil management practices. We will identify the soil health characteristics that are most closely related to soybean yield variation and explore the extent to which the simulation model captures the effect of soil health on yields. The outreach part of our project will create an awareness of the potential of crop modeling in advanced crop management and share with producers and their consultants the potential for sustainable soil management to improve soybean yields and profitability.
Specifically, the objectives are:
– Identify key soil health indicators that relate to soybean yield on commercial farms of PA. (in progress)
– Evaluate and improve if needed the cropping systems model CYCLES at simulating the effects of soil health on soybean yields in the selected fields. (in progress)
– Evaluate the impact of soil management history on soil health indicators and soybean yields. (in progress)
– Create awareness among crop producers and consultants about the importance of soil health for crop yields and the ability to use crop modeling to make more sustainable crop management decisions. (in progress)
Using field and modeling techniques, we will identify soil health attributes that are most closely related to soybean yield to both provide agronomists with indicators to use for field estimates of soybean yield potential and to improve model simulations. The outreach component will use multiple approaches, including newsletter articles, field days, and a workshop, to engage consultants and producers in learning about the research results and the potential for models to inform sustainable soil and crop management.
We are still processing and analyzing the samples we collected this year to soon proceed with the data analysis necessary to reach our goals. We finished collecting plant and soil samples by the last week of November in Centre county – PA. In 2017, we worked closely with 4 farmers from Lebanon county and 3 farmers from Centre county – totaling 13 soybean fields worth of data.
The preliminary data we will present in the current report is the relationship between Saturated Hydraulic Conductivity (Ksat) and soybean yield. Soybean yield was estimated by cutting 3-m of plants in each sampling unit, and the grain was weighed after threshing the samples. Yields were converted to 13% grain moisture. The Ksat data was collected in 11 fields mostly in spring, but we only completed the dataset with a few readings (1 per farm) after harvest in the fall. The Ksat was measured using an automated infiltrometer developed by METER, called SATURO. At least 1 reading (maximum 3) per sampling unit was related to the soybean yield of the same sampling unit. The Ksat is a measure of how ease the water moves in the soil, and it is an indirect measurement of soil structure and therefore soil health. Since it is a saturated measurement, soil moisture at the time of measurement in theory should not affect the results. Soil texture is a key determinant of soil hydraulic conductivity, and we intend to use soil texture as a covariate to analyze the data. Therefore, this analysis will only be completed after we finish particle size analysis.
This year, precipitation was abundant in Lebanon and Centre counties, and water stresses were not a problem in most fields. Nonetheless, soybean yields varied considerably among all 65 sampling units (13 fields) in 2017 – from 21 to 129 bushels/acre. As expected, highest yields occurred in Lebanon county. Ksats explained 53% of the soybean yield variability when considering the averages of the 11 fields evaluated (graph 1). If we average the Ksats and soybean yields per farm (6), the R2 increases to 0.85. The fields with the highest Ksats frequently showed the presence of earthworms (figure), whereas the lowest Ksats were found in fields either with compacted soils or suffering from soil crusting. In theory, the Ksat can not only be used to assess surface runoff, but also to indirectly evaluate how easy the roots can explore soil layers, and hence water and nutrients. We are still working on this data, but it seems that Ksat could become an important predictor of soybean yield in this project, and therefore an important indicator of soil health.
No conclusions yet.
Education & Outreach Activities and Participation Summary
We were invited to discuss this project and the benefits of soil health for sustainable soybean yield production in three events/field days:
- Centre County: Penn State Diagnostic Clinic on July 19 and 20th – organized by Penn State Extension;
- Lebanon County (Glenn Krall farm – August 23rd) and Bradford County (Matthew Ahern farm – August 24th) – organized by the on-farm soybean network and sponsored by the PA soybean board;