A solution for sampling position errors in maize and soybean root mass and length estimates

Root mass and length attributes are difficult to obtain in the field and currently there is uniformity among literature studies in estimating the effect of sampling position error. With the objectives of 1) quantifying the sampling position error in calculating weighted average root values per unit area and 2) developing an algorithm to minimize root position sampling error so that existing data in the literature can be used in future studies, we collected and analyzed root mass and length data across four sampling positions (0, 12, 24 and 36 cm distance from the plant row; row-to-row spacing 76 cm) from two maize and two soybean fields in central Iowa, USA. In-row sampling position (i.e., 0 cm from the plant row) over-estimated root mass and length by 66% and 46% for maize and soybean, while cores taken in the middle of plant rows (i.e., 36 cm from the plant row) under-estimated root mass and length by 34% and 23% for maize and soybean. As sampling distance from the plant row increased from 0 to 36 cm, maize root mass declined four times faster than soybean, while root length declined at almost the same rate between crops. Sampling 10 cm from the plant row provided the closest estimate to the weighted average value in both crops. We developed a new algorithm that predicts weighted average root attributes values with a R2 of 0.93 for mass and a R2 of 0.70 for length. The algorithm requires two user inputs (the measured root attribute value and the distance from the plant row). The new algorithm was tested across diverse environments, cultivars, and management practices and proven accurate for subsequent use (R2 = 0.70 and R2 = 0.87 for mass and length). This study provides guidance to strategically sample roots in future row crop research and an algorithm to eliminate sampling position bias in existing data

Maize root distributions strongly associated with water tables in Iowa, USA

This article is published as Nichols, Virginia A., Raziel A. Ordóñez, Emily E. Wright, Michael J. Castellano, Matt Liebman, Jerry L. Hatfield, Matt Helmers, and Sotirios V. Archontoulis. "Maize root distributions strongly associated with water tables in Iowa, USA." Plant and Soil (2019). doi: 10.1007/s11104-019-04269-6.</p

Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest

Root traits are important to crop functioning, yet there is little information about how root traits vary with shoot traits. Using a standardized protocol, we collected 160 soil cores (0−210 cm) across 10 locations, three years and multiple cropping systems (crops x management practices) in Iowa, USA. Maximum root biomass ranged from 1.2 to 2.8 Mg ha−1 in maize and 0.86 to 1.93 Mg ha−1 in soybean. The root:shoot (R:S) ratio ranged from 0.04 to 0.13 in maize and 0.09 to 0.26 in soybean. Maize produced 27 % more root biomass, 20 % longer roots, with 35 % higher carbon to nitrogen (C:N) ratio than soybean. In contrast, soybean had a 47 % greater R:S ratio than maize. The maize R:S ratio values were substantially lower than literature values, possibly due to differences in measurement methodologies, genotypes, and environment. In particular, we sampled at plant maturity rather than crop harvest to minimize the effect of senescence on measurements of shoots and roots. Maximum shoot biomass explained 70 % of the variation in root biomass, and the R:S ratio was positively correlated with the root C:N measured in both crops. Easily-measured environmental variables including temperature and precipitation were weakly associated with root traits. These results begin to fill an important knowledge gap that will enable better estimates of belowground net primary productivity and soil organic matter dynamics. Ultimately, the ability to explain variation in root mass production can be used to improve C and N budgets and modeling studies from crop to regional scales

Understanding the 2016 yields and interactions between soils, crops, climate and management

Several technologies to forecast crop yields and soil nutrient dynamics have emerged over the past years. These include process-based models, statistical models, machine learning, aerial images, or combinations. These technologies are viewed as promising to assist Midwestern agriculture to achieve production and environmental goals, but in general, most of these technologies are in their initial stages of implementation. In June 2016 we launched a web-tool (http://crops.extension.iastate.edu/facts/) that provided real-time information and yield predictions for 20 combinations of crops and management practices. Our project, which is called FACTS (Forecast and Assessment of Cropping sysTemS), takes a systems approach to forecast and evaluate cropping systems performance. In this paper we report FACTS yield predictions accuracy against ground-truth measurements and analyzing factors responsible for achieving 200-240 bu/acre corn yield and 55-75 bu/acre soybean yields in the FACTS plots in 2016

Water availability, root depths and 2017 crop yields

During 2016 and 2017, June-July precipitation was below normal in many parts of Iowa creating midseason concerns about potential yield loss due to water stress. However, these concerns were not realized. In contrast, 2016 and 2017 crop yields over-performed yields obtained in many years with average of above average June-July precipitation. In Iowa, deep root systems, high soil water storage capacity, and shallow water tables are common explanations for high yields in years with below normal precipitation. How deep can roots grow? How much does groundwater contribute to the yields? To answer these questions and more, the Forecast and Assessment of Cropping sysTemS (FACTS) project was established in 201

Maize and soybean root front velocity and maximum depth in Iowa, USA

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10–15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 ± 0.2 cm d−1 until the 5th leaf stage when it increased to 3.12 ± 0.03 cm d−1 until maximum depth occurred at the 18th leaf stage (860 °Cd after planting). Similar to maize, soybean RFV was 1.19 ± 0.4 cm d−1 until the 3rd node when it increased to 3.31 ± 0.5 cm d−1 until maximum root depth occurred at the 13th node (813.6 °C d after planting). The maximum root depth was similar between crops (P \u3e 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89–90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R2 \u3e 0.76; P \u3c 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment

In-Season Forecasting of Plant Growth, Soil Water-Nitrogen, and Grain Yield

In 2015, a yield forecasting project was initiated with the objective of forecasting in-season soil water-nitrogen dynamics, in-season plant growth, and end-of-season grain yields. This concept was initiated to help farmers and agronomists make in-season management decisions, in addition to the ability to look back on the growing season to see what management practices could have been changed to improve grain yields and net profits, but also reduce nitrogen loss.</p

Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest

Root traits are important to crop functioning, yet there is little information about how root traits vary with shoot traits. Using a standardized protocol, we collected 160 soil cores (0−210 cm) across 10 locations, three years and multiple cropping systems (crops x management practices) in Iowa, USA. Maximum root biomass ranged from 1.2 to 2.8 Mg ha−1 in maize and 0.86 to 1.93 Mg ha−1 in soybean. The root:shoot (R:S) ratio ranged from 0.04 to 0.13 in maize and 0.09 to 0.26 in soybean. Maize produced 27 % more root biomass, 20 % longer roots, with 35 % higher carbon to nitrogen (C:N) ratio than soybean. In contrast, soybean had a 47 % greater R:S ratio than maize. The maize R:S ratio values were substantially lower than literature values, possibly due to differences in measurement methodologies, genotypes, and environment. In particular, we sampled at plant maturity rather than crop harvest to minimize the effect of senescence on measurements of shoots and roots. Maximum shoot biomass explained 70 % of the variation in root biomass, and the R:S ratio was positively correlated with the root C:N measured in both crops. Easily-measured environmental variables including temperature and precipitation were weakly associated with root traits. These results begin to fill an important knowledge gap that will enable better estimates of belowground net primary productivity and soil organic matter dynamics. Ultimately, the ability to explain variation in root mass production can be used to improve C and N budgets and modeling studies from crop to regional scales.This article is published as Ordóñez, Raziel A., Sotirios V. Archontoulis, Rafael Martinez-Feria, Jerry L. Hatfield, Emily E. Wright, and Michael J. Castellano. "Root to shoot and carbon to nitrogen ratios of maize and soybean crops in the US Midwest." European Journal of Agronomy 120 (2020): 126130. doi: 10.1016/j.eja.2020.126130.</p

Evaluating maize and soybean grain dry-down in the field with predictive algorithms and genotype-by-environment analysis

A delayed harvest of maize and soybean crops is associated with yield or revenue losses, whereas a premature harvest requires additional costs for artificial grain drying. Accurately predicting the ideal harvest date can increase profitability of US Midwest farms, but today’s predictive capacity is low. To fill this gap, we collected and analyzed time-series grain moisture datasets from field experiments in Iowa, Minnesota and North Dakota, US with various maize (n = 102) and soybean (n = 36) genotype-by-environment treatments. Our goal was to examine factors driving the post-maturity grain drying process, and develop scalable algorithms for decision-making. The algorithms evaluated are driven by changes in the grain equilibrium moisture content (function of air relative humidity and temperature) and require three input parameters: moisture content at physiological maturity, a drying coefficient and a power constant. Across independent genotypes and environments, the calibrated algorithms accurately predicted grain dry-down of maize (r2 = 0.79; root mean square error, RMSE = 1.8% grain moisture) and soybean field crops (r2 = 0.72; RMSE = 6.7% grain moisture). Evaluation of variance components and treatment effects revealed that genotypes, weather-years, and planting dates had little influence on the post-maturity drying coefficient, but significantly influenced grain moisture content at physiological maturity. Therefore, accurate implementation of the algorithms across environments would require estimating the initial grain moisture content, via modeling approaches or in-field measurements. Our work contributes new insights to understand the post-maturity grain dry-down and provides a robust and scalable predictive algorithm to forecast grain dry-down and ideal harvest dates across environments in the US Corn Belt