Non Technical Summary
Our civilization depends on continuously increasing levels of agricultural productivity, which itself depends on (among other things) the interplay of crop varieties and the environments in which these varieties are grown. Hence, to increase agricultural productivity and yield stability, it is necessary to develop improved crop varieties that deliver ever more yield, even under the variable weather conditions induced by global climate change, all the while minimizing the use of inputs such as fertilizers that are limiting, expensive or have undesirable ecological impacts.By coupling a network of innovative, low-cost nitrate sensors across multiple environments within the heart of the corn belt and advanced cropping systems modeling (APSIM, the most widely used modeling platform), the proposed research will enhance our understanding of and ability to predict yield and Genotype x Environment interactions. The integration of nitrate (N) dynamics into this model is expected to greatly increase the accuracy of its predictions. Because we will also integrate genotypes into this model, the proposed research outlines a new and innovative approach for breeding crops that exhibit increased yields and yield stability. It will be possible to readily translate this approach to other crops.By generating data on nitrate concentrations in soil and in planta at unprecedented spatial and temporal resolution at multiple sites with different soil characteristics and weather, the proposed research will also improve our understanding of N cycles in both the soil and plant. Although essential to plant growth and high yields, when over-applied N can result in a variety of serious negative externalities, some of which are currently the subject of high-impact litigation in Iowa. Project outcomes have the potential to provide guidance to farmers about how to apply sufficient but not excessive amounts of N fertilizer, resulting in both economic benefits to farmers and positive environmental externalities.Our focus on creating a new approach to breeding for yield stability meets the USDA sustainability goals to "satisfy human food and fiber needs" and "sustain the economic viability of farm operations". Our focus on nitrogen meets the USDA sustainability goals to "enhance environmental quality" and to "make the most efficient use of nonrenewable resources...and integrate, where appropriate, natural biological cycles and controls". More specifically, this proposal addresses the NIFA-Commodity Board co-funded priority for "development and application of tools to predict phenotype from genotype" and the "the development of high-throughput phenotyping equipment and methods".
Animal Health Component
Research Effort Categories
Goals / Objectives
Our major goals are:Specific Aim 1. Calibrate existing soil and in planta nitrate sensorsSpecific Aim 2. Integrate nitrate and genotypic data into an existing crop modelSpecific Aim 3. Test predictive ability of genotype-enable crop modelThis integration of genotyping data, plant-soil N sensing data, and cropping systems modeling will be transformative for plant breeding. Although the limited number of genotypes to be characterized during this project will not be sufficient to enable us to identify specific chromosomal regions that influence plant responses to the environment (including N concentrations in soil and in planta), we anticipate that it will be possible to predict yield and yield stability of diverse genotypes in diverse environments. This predictive ability will be tested using existing data from the G2F Initiative that will be available to the project at no cost. Past efforts to link genotypes to crop modeling were largely focused on plants that were not water- or N-stressed or plants that were only water-stressed. In these studies, N was assumed to be non-limiting for two reasons: a) the complexity in phenotyping and modeling water-nitrogen stressed plants and b) a lack of high resolution soil and in planta N data that limited our ability to understand and predict N dynamics. In this project we aim to fill this important gap and thereby advance modeling efforts and the application of this technology to breeding.
The proposed project will make use of 5 locations where members of our team have calibrated the soil part of the Agricultural Production Systems sIMulator (APSIM) cropping systems model. The locations comprise the Forecast and Assessment of Cropping Systems project (FACTS http://crops.extension.iastate.edu/facts/) established in 2016. At each location, water, nitrogen, and corn growth and yield are modeled and predicted with weekly public releases on the website. This project combines for the first time high-resolution soil and crop measurements with modeling and has established the link between modelers and crop and soil scientists. The proposed project will incorporate genotypic data into these models, thereby engaging geneticists and breeders.Specific Aim 1. Calibrate existing soil and in planta nitrate sensors: Deploy and calibrate innovative Micro-Electro-Mechanical Systems (MEMS)-based nitrate sensors for both soil and plants to generate data from yield trials of hybrids with known genotypes in multiple, well-defined environments. We will calibrate nitrate sensors to our field conditions. Our soil and in planta nitrate sensors will be integrated into a powerful field-based climate-proof sensor system that will turn on and off at assigned intervals to collect data. We will deploy soil and plant N sensor system in the field on a select set of inbreds and collect data to calibrate for accurate nitrate measurements and to determine optimized sensor layout design.Specific Aim 2. Integrate nitrate and genotypic data into an existing crop model: Integrate the resulting data into an advanced cropping systems modeling platform that can link genetic and environmental data to predict how vast numbers of plant, soil, management and weather-based data points interact to control plant phenotypes across thousands of possible scenarios (most of which have not been empirically tested). We will deploy more sensors in more location with more inbreds. Sensor-based model outputs of nitrogen dynamics and yield will be tested against harvest-based "ground truth" measurements of yield and the prediction accuracy will be recorded. Then we will use the model to explore the system by running thousands of simulations (combinations of different model parameters, management practices and weather conditions) to a) identify the most critical model parameters (measurable traits) that affect yield stability and b) understand GxE interactions that affect relevant phenotypes.Specific Aim 3. Test predictive ability of genotype-enable crop model: Test the ability of the advanced cropping systems modeling platform to accurately predict yields of hybrids that were not included in the model but whose genotypes are known. Because it is not possible to conduct MEMS-based calibration of cropping systems models for all possible hybrids, we will assess the efficacy of extending our calibrated models using genomic information in two ways to generate coeffiicents. These coefficients will then be used to generate model-based predictions for yield and compared to experimentally determined values available from G2F for these hybrids. These cropping systems model-based predictions will be compared with predictions from other prediction models that incorporate genomic and environmental information.The dissemination of the results of this funded research will be accomplished via presentations at national professional conferences and scientific publication. In addition, this project will train two graduate students at the intersection of sensors, crop modeling and genetics, helping to train "next generation" plant breeders.