Evaluating Strip Tillage and Fertility Placement to Reduce Soil and P Loss

Principal Investigator

Ben Rosser

Research Institution

Ontario Ministry of Agriculture and Rural Affairs (OMAFRA)

Timeline

April 1, 2018 – December 31, 2021 Continue reading “Evaluating Strip Tillage and Fertility Placement to Reduce Soil and P Loss”

Red clover non-uniformity: field assessment of drought tolerant red clover, delayed seeding strategies and spatial nitrogen application

Principal Investigator

Ralph C. Martin

Research Institution

University of Guelph

External Funding Partners

OMAFRA/U of Guelph partnership

Project Start

April 2012

Project End

March 2015

Objectives

  • Assess whether red clover stand variation observed in Ontario is predominantly due to drought related factors or to other factors.
  • Determine if there is genetic variation for drought tolerance within red clover that can be exploited to improve uniformity of persistence under field conditions.
  • Assess whether over seeding red clover after wheat anthesis will improve red clover stand uniformity.
  • Test existing technology for image acquisition of non-uniform stands and nitrogen application equipment to apply nitrogen in proper amounts to specific areas, with and without clover.

Impact

  • The incorporation of red clover into corn/soybean/wheat rotations may lead to positive soil benefits such as increased soil organic matter and a nitrogen credit.
  • The use of variable N applications and image acquisition methods to assess red clover non-uniformity may reduce nitrous oxide (N2O) emissions in corn when non-uniform red clover stands have a uniform application N application in corn.

Scientific Summary

The benefits of uniform stands of red clover in a rotation are well established. Nitrogen fertilizer reductions, crop yield increases of crops immediately following red clover and also subsequent crops in the rotation, soil quality improvements and soil carbon increases have been well documented. There has been resurgence in the use of red clover. Although red clover overseeded to winter wheat has increased, there is an ongoing problem of non-uniformity of red clover stands. When farmers are confronted with non-uniform stands, they may respond by applying nitrogen without any consideration of red clover nitrogen contributions. This is a serious environmental concern, because it results in high nitrogen, high carbon zones in the field where red clover did establish, thus leading to increased risk of nitrous oxide (N2O) emissions. Previous research has identified a number of factors that contribute to non-uniformity and strategies to improve probability of uniformity; however, drought related factors, which cannot be managed, were suggested as primary causes of non-uniformity.

This project investigated the role of water stress as a contributing factor to non-uniformity in red clover and evaluated a system to reduce the risk of N2O emissions in corn. Lower than average precipitation in the red clover growing season negatively affects average red clover biomass and stand densities, and it increases the degree of non-uniformity in red clover biomass and stand densities. At locations with the most uniform stands of red clover, variation in red clover biomass was linked more to variation in moisture than to variation in stand densities. We evaluated several machine learning classifiers for generating full-field red clover maps given an aerial mosaic image and sparse ground truth data. The system distinguished between red clover ground cover and ground cover of volunteer winter wheat, oil seed radish and bare soil. The accurate and robust system represents a tool that could be used by commercial producers for variable rate applications of nitrogen to corn following non-uniform stands of red clover to reduce the risk of N2O emissions.

Precision agriculture and intensive production systems

Principal Investigator

Tony Balkwill

Research Institution

NithField Advanced Agronomy

Project Start

March 2012

Project End

February 2016

Objectives

  • Compare traditional potash (K; potassium) applications with two precision agriculture systems of variable rate based K applications: 1) based on 2.5 acre GPS grid samples, targeting a certain ppm’s soil test level; and 2) based on the previous yield or crop removal amounts, not looking at soil test results.
  • Investigate the economics of the different fertility applications methods of K.
  • Determine if the increase in cost and time of precision agriculture system variable rate K have an increase in return (e.g. measure the performance of precision agriculture system).

Impact

  • The determination whether advanced application systems have an increased return on investment (ROI) when applying nutrients to fields in an acre specific manner will allow farmers to be more efficient in their use of potash (K) fertilizer.
  • The improved understanding of soil systems and soil fertility will allow researchers to determine if the prescription using variable rate fertilizer are environmentally responsible and applying the right rate to the right area.

Scientific Summary

Precision Agriculture has come to be a very broad word used to describe many technological advances in agriculture. We have seen the adaptation of technology in agriculture grow over the years. As implementation and understanding have developed with farmers other retail industries have directed agronomic practices to target this new skill set. Historically fields were farmed as a whole. We can now target areas within each field to address the specific needs of those regions. We can measure the economics and environmental changes more and more accurately every season. Currently we see the technology being very easy to work with and widely available. However there is a gap in bridging old practices into these new precise systems. Many recommendations don’t take in effect the ability technology has, so the risk to growers is using precision agronomics like variable rate fertilizer and have no way of knowing if the “prescription” for their field is correct.

This research project investigated Variably Controlled Agronomic Precision options currently available for variable rate fertilizer prescriptions of Potassium compared to conventional one rate approaches. The project also looked at methods and procedures to utilize yield maps into management decisions. The goal of the study was to learn the outcome, challenges and results of variable rate fertilizer application methods. With the completion of the research project we uncovered some key information needed when building and or using “prescription K applications.” Firstly, the recommendations were based off of old “one rate” programs. We see quite quickly that if you’re using just the ppm of a soil test to get recommendations they would not the right rate needed for that area of the farm. We noticed with the prescriptions we were applying way too much to certain soils and not enough to others. Some of the outcomes were very significant in a few areas. Firstly, we determined that Variable Rate potash applications need to be based on both cation exchange capacity (CEC) and ppm K test to see both economic and fertility return. Secondly, the crop removal variable rate application, which takes no soil test data into account, is specific to not only crops (soybeans, wheat, corn) but also is specific to varieties and cultivars. That is, certain genetic lines seemed to use more K as luxury consumption, etc. This was an observation and was not tested fully in the trial. Further works need to validate this result. Moving forward with more large scale field sized research will be the foundation to understand the true economics, environmental and sustainability of intensive agriculture. We can’t learn precision agronomics in small plot research systems. They need to capture large scale field variability and equipment utilization. This will be the challenge in our local industry, shifting out of historical research ideas into a new area of study that will have some unknown challenges of its own to work through, but also the uptake and development of newer technologies need to be measured and understood. Technology will be the answer to efficient sustainably farming, but without careful invested research it could quickly develop into a problem.