Location: Adaptive Cropping Systems Laboratory
Project Number: 8042-11660-001-003-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Jun 17, 2019
End Date: Dec 31, 2020
The overarching goal of this project is to develop a Potato Early-Die (PED) simulation model based on tuber yield stability and soil health indicators. As an integrated group of researchers at Michigan State University, University of Wisconsin and ARS Beltsville, we propose to test the capability of remote sensing technologies from several different platforms (UAV, airborne and satellites) to detect and monitor differences in PED disease, nematode infestation and various soil parameters that affect potato growth and yield. Through this research we aim to better identify and understand the conditions that lead to within-field variability of PED and related the spatial patterns to soil health indicators, and yield stability map, as shown in preliminary results. Research Hypotheses are as follows: 1. PED is correlated with spatial variation of soil quality, thermal and yield stability maps; 2. Within season imagery coupled with long-term stability maps can predict soil health indicators and be used to target soil zones of different thermal behavior and historical yield stability; 3.Crop models with a novel indicator of PED susceptibility can better estimate spatial variability of potato tuber yields on both a inter- and intra-field basis.
Soil samples will be collected from replicates of each of three potato yield stability zones selected at random from two 2019 commercial chip potato fields, one in Michigan and one in Wisconsin. The samples will be analyzed for the twelve Cornell University soil health indicators plus the potato early-die disease (PED) pathogens. Hand-dug tuber yields, and early-die symptom assessment will be done. Soil health indicators and remote sensing imagery (optical and thermal) will be used to develop a novel spatial soil health indicator of PED susceptibility to be integrated into three crop models: SALUS, SPUDSIM and SUBSTOR. Parameters of plant growth and root dynamics will be adjusted in these models depending on the indicators to improve the predicted rate of senescence, and its intensity, using remote sensing optical and thermal imagery. Model performances of final yield estimations will be compared against current versions.