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Accurate estimation of nitrogen (N) balance (a measure of potential N losses) in producer fields requires information on grain N concentration (GNC) to estimate grain-N removal, which is rarely measured by producers. The objectives of this study were to (i) examine the degree to which variation in GNC can affect estimation of grain-N removal, (ii) identify major factors influencing GNC, and (iii) develop a predictive model to estimate GNC, analyzing the uncertainty in predicted grain-N removal at field and regional levels. We compiled GNC data from published literature and unpublished databases using explicit criteria to only include experiments that portray the environments and dominant management practices where maize is grown in the US North Central region, which accounts for one-third of global maize production. We assessed GNC variation using regression tree analysis and evaluated the ability of the resulting model to estimate grain-N removal relative to the current approach using a fixed GNC. Across all site-year-treatment cases, GNC averaged 1.15%, ranging from 0.76 to 1.66%. At any given grain yield, GNC varied substantially and resulted in large variation in estimated grain-N removal and N balance. However, compared with GNC, yield differences explained much more variability in grain-N removal. Our regression tree model accounted for 35% of the variation in GNC, and returned physiologically meaningful associations with mean air temperature and water balance in July (i.e., silking) and August (i.e., grain filling), and with N fertilizer rate. The predictive model has a slight advantage over the typical approach based on a fixed GNC for estimating grain-N removal for individual site-years (root mean square error: 17 versus 21 kg N ha−1, respectively). Estimates of grain-N removal with both approaches were more reliable when aggregated at climate-soil domain level relative to estimates for individual site-years.

The rainfed USA Midwestern region has deep, fertile soils and leads the USA in soybean [Glycine max, (L.) Merr.] production. Biological nitrogen (N) fixation (BNF) contributes a portion of the soybean N requirement, but variability in BNF is poorly understood and estimates of BNF for this region are rare. We established experiments in Iowa, USA to gain a better understanding of BNF and increase its predictability. We collected in-season BNF measurements accompanied by high temporal resolution soil and plant growth measurements. Across two years, two locations and two planting dates, we found that BNF contributed 23–65% of total aboveground N accumulation in soybean. The BNF rate was maximized at the early seed-filling period and varied from 1 to 3 kg N ha−1day−1. During seed filling period, the rate of BNF was related to crop growth rate (carbon (C) supply) but not to N accumulation by the reproductive organs (N demand). We found that a minimum crop growth rate of 135 kg dry matter ha−1day-1 is required to sustain maximum BNF rates. In contrast to BNF, the soil inorganic N uptake rate was related to seed N demand but not to C supply. Biomass production was the best predictor of total soybean BNF (R2 > 0.83). On average, 0.013 kg N was fixed per kg biomass produced. Across all trials, the N exported via seed was greater than the N imported via BNF, which suggests that Midwest US soybeans may reduce soil organic matter. We concluded that future research efforts should focus on increasing C – rather than N – availability during the seed filling period towards improving both grain yields and environmental sustainability.

Narrowing row width in soybean fields leads to earlier canopy closure, which may increase capture of incoming solar radiation during critical crop stages for yield determination. Theoretically, this should enhance seed yield. However, in prior studies, the impact of narrowing row spacing on soybean yield has been inconsistent. To explore on a broader scale the potential factors underlying this inconsistency, we evaluated the yield difference between narrow (NR; ≈38 cm) and wide (WR; ≈76 cm) row spacing using two sources of yield and management information: (i) data collected from 4879 soybean production fields via a multi-year, multi-state survey of soybean producers in the North Central US region; and (ii) data extracted from 129 site-year experiments that quantified NR-WR yield difference. The producer fields were allocated to their respective climate-soil domains to enable analysis of the NR-WR yield difference within each domain. The experimental trial data originated from three US geographic regions: south, central, and north. Key crop developmental stages in each trial were estimated using a soybean crop simulation model to discern if changes in crop phenology or any weather variable occurring before versus after a specific crop stage modulated the magnitude of the NR-WR yield difference. Analysis of experimental trial data indicated that, while NR yields were overall higher than WR yields, the NR-WR yield difference varied by region: 540 (south), 104 (central), and 240 kg ha−1 (north); the respective NR yields were greater than WR yields in 92%, 68%, and 84% of the cases. In the north and south regions, the NR-WR yield difference increased when the crop cycle length decreased as a consequence of later sowing date, earlier cultivar maturity group, and/or higher temperature. The relatively smaller (and occasionally negative) NR-WR yield difference detected in the central region was likely the result of environmental conditions that favored canopy closure irrespective of row spacing. In contrast to the analysis of the experimental database, no consistent NR-WR yield differences were detected in the producer field database. We hypothesize that the apparent absence of a significant NR-WR effect in the producer dataset is likely associated with the background management used with narrow spacing, together with yield losses due to wheel damage and greater disease pressure. This complementary approach using both producer and experimental data can help evaluate if practices documented in experimental trials to enhance yield realize equivalent yield increases in producer fields and, if not, explore underlying causes for the discrepancy.

The use of swine manure as a source of plant nutrients is one alternative to synthetic fertilizers. However, conventional manure application with &gt, 90% water and a low C:N ratio results in soil C loss to the atmosphere. Our hypothesis was to use biochar as a manure nutrient stabilizer that would slowly release nutrients to plants upon biochar-swine manure mixture application to soil. The objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and plant-available macro- and micronutrients in greenhouse-cultivated corn (Zea mays L.) and soybean (Glycine max (L.) Merr.). Neutral pH red oak (RO), highly alkaline autothermal corn stover (HAP), and mild acidic Fe-treated autothermal corn stover (HAPE) biomass were pyrolyzed to prepare biochars. Each biochar was surface-applied to swine manure at a 1:4 (biochar wt/manure wt) ratio to generate mixtures of manure and respective biochars (MRO, MHAP, and MHAPE). Conventional manure (M) control and manure-biochar mixtures were then applied to the soil at a recommended rate. Corn and soybean were grown under these controls and treatments (S, M, MRO, MHAP, and MHAPE) to evaluate the manure-biochar impact on soil quality, plant biomass yield, and nutrient uptake. Soil organic matter significantly (&lt, 0.05) increased in all manure-biochar treatments, however, no change in soil pH or total N was observed under any treatment. No difference in soil ammonium between treatments was identified. There was a significant decrease in soil Mehlich3 (M3) P and KCl extractable soil NO3− for all manure-biochar treatments compared to the conventional M. However, the plant biomass nutrient concentrations were not significantly different from control manure. Moreover, an increasing trend of plant total N and decreasing trend of P in the plant under all biochar-manure treatments than the controls were noted. This observation suggests that the presence of biochar is capable of influencing the soil N and P in such a way as not to lose those nutrients at the early growth stages of the plant. In general, no statistical difference in corn or soybean biomass yield and plant nutrient uptake for N, P, and K was observed. Interestingly, manure-biochar application to soil significantly diluted the M3 extractable soil Cu and Zn concentrations. The results attribute that manure-biochar has the potential to be a better soil amendment than conventional manure application to the soil.

Radiation use efficiency (RUE) is difficult to estimate and unreasonable to perform on a small plot scale using traditional techniques. However, the increased availability of Unmanned Aerial Vehicles (UAVs) provides the ability to collect spatial and temporal data at high resolution and frequency, which has made a potential workaround. An experiment was completed in Iowa to (i) demonstrate RUE estimation of soybean [Glycine max (L.) Merr.] from reflectance data derived from consumer-grade UAV imagery and (ii) investigate the impact of foliar fungicides on RUE in Iowa. Some fungicides are promoted to have plant health benefits beyond disease protection, and changes in RUE may capture their effect. Frogeye leaf spot severity did not exceed 2%. RUE values ranged from 0.98 to 1.07 and 0.96 to 1.12 across the entire season and the period post-fungicide application, respectively, and fell within the range of previously published soybean RUE values. Plots treated with fluxapyroxad + pyraclostrobin had more canopy cover (p = 0.078) compared to the non-treated control 133 days after planting (DAP), but yields did not differ. A &ldquo, greening effect&rdquo, was detected at the end of the sample collection. RUE estimation using UAV imagery can be considered a viable option for the evaluation of management techniques on a small plot scale. Since it is directly related to yield, RUE could be an appropriate parameter to elucidate the impact of plant diseases and other stresses on yield.

Ureteral inguinal hernias are a well-described entity, within the spectrum of sliding hernias, with over 140 cases described since 1880. Though herniation of the ureter is relatively rare and complete ureteric obstruction is infrequent, a massive herniation may cause complete obstruction, leading to hydronephrosis. Management of these hernias is challenging and poses a significant danger of inadvertent injury and entrapment of a tortuous ureter. When faced with this type of hernia, extreme care should be taken to perform the appropriate preoperative workup and thoroughly plan the surgical approach. The present case describes a patient with a known ureteral inguinal hernia, who underwent a laparoscopic repair of the hernia with mesh placement.

Biochar application to the soil can improve soil quality and nutrient leaching loss. Recent studies have reported that surficial application of biochar to stored swine manure can reduce emissions of odorous compounds and reduce the volatilization loss of ammonia. Our working hypothesis was that the biochar-treated manure application to the soil would decrease nutrient leaching from manure and increase plant-available nutrients. The study objectives were to evaluate the impact of biochar-treated swine manure on soil total C, N, and other major and minor nutrients. Three biochars (i) neutral pH red-oak (RO), (ii) highly alkaline autothermal corn (Zea mays) stover (HAP), and (iii) mild acidic Fe-treated autothermal corn stover (HAPE) were incubated with swine manure for a month. The biochar-manure mixture was applied in triplicate to soil columns with application rate determined by the P2O5-P content in manure or manure-biochar mixtures after the incubation. The ammonium (NH4+), nitrate (NO3-), and reactive P concentrations in soil column leachates were recorded for eight leaching events. Soil properties and plant-available nutrients were compared between treatments and control manure & soil. Manure-(HAP&HAPE) biochar treatments significantly increased soil organic matter (OM) and increased soil total C, N, and improved soil bulk density. Concentrations of KCl-extractable NH4+ and NO3- significantly increased in HAPE column leachates during this 4-week study and in the soil after the experiment. A significant reduction in soil Mehlich3 Cu was also observed for the manure-HAPE mixture compared with the control. Overall, the manure-biochar incubation enabled biochar to sorb nutrients from manure, and the subsequent manure-biochar mixture application to soil improved soil quality and plant nutrient availability in comparison to conventional manure application to soil. This proof-of-the-concept study suggests that biochars could be used to solve both environmental and agronomic challenges and further improve the sustainability of animal and crop production agriculture.

A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge for precision agriculture is that these factors interact with one another on a subfield scale. Seeding density and nitrogen (N) fertilizer application rates are two of the most important inputs influencing agronomic, economic and environmental outcomes in cropping systems including yield, return on investment (ROI), and nitrate (NO3−) leaching. Here a cropping system model framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates based on publicly available data sources. The framework is used estimate differences in yield, ROI, NO3− leaching, and N2O emissions corresponding with economic optimum (maximum ROI) and agronomic optimum (maximum yield) inputs. The framework couples the process-based APSIM cropping system model with the SSURGO soils database, Daymet weather data service, land grant university estimates of crop production costs and commodity price estimates, and the R statistics software. Framework performance was evaluated using multiple years of precision yield monitor data obtained from a conventionally managed continuous maize (Zea mays L.) cropping system field located in north central Iowa on which varying N-fertilizer rates were applied. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r2 = 0.65 and a root mean square error of 1645.0 kg ha−1. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated an average ROI benefit of 7.2% as well as an average NO3− leaching and N2O emissions reductions of 2.5 and 7.6 kg ha−1, respectively. However, in a minority of cases NO3− leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and will likely lead to improved environmental outcomes.

Field trials are commonly used to estimate the effects of different factors on crop yields. In the present study, we followed an alternative approach to identify factors that explain field-to-field yield variation, which consisted of farmer survey data, a spatial framework, and multiple statistical procedures. This approach was used to identify management factors with strongest association with on-farm soybean yield variation in the US North Central (NC) region. Field survey data, including yield and management information, were collected over two crop growing seasons (2014 and 2015) from rainfed and irrigated soybean fields (total of 3568 field-year observations). Fields were grouped into technology extrapolation domains (TEDs) that accounted for soil and climate variation and 9 TEDs were selected based on the number of fields needed to detect yield differences due to management as determined using power analysis. Average yield ranged from 2.5 to 5 Mg ha−1 across TEDs, with field yield distributions in half of the domains having a distributional peak that was close to maximum yields. Conditional inference trees analysis was chosen among 26 statistical procedures as the approach that best combines ability to detect and rank factors (and their interactions) with greatest influence on on-farm yield and relatively easy interpretation of results. Survey data from ca. 150 fields in each of the nine TEDs allowed us to identify key management factors influencing yields for an agricultural area that includes ca. 7 million ha sown with soybean. In five of the nine TEDs, highest yields were observed in early-sown fields. Other factors explaining on-farm yield variation were maturity group, and in-season foliar fungicide and/or insecticide application, but, in some cases, their influence on yield depended upon sowing date and water regime. While the approach proposed here cannot establish cause-effect relationships conclusively, it can certainly provide a focus to replicated field experiments in relation to which management factors to investigate. We believe that future agronomic studies based on farmer survey data can greatly benefit from ex-ante identification of most important TEDs (relative to crop area and production) as well as determination of minimum number of farmer survey data that needs to be collected from each of them based on expected yield differences and variability. The approach is generic enough to be applied in other crop producing regions as long as farmer data and associated climate and soil databases are available.

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.

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 > 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 > 0.76; P

Identification of causes of gaps between yield potential and producer yields has been restricted to small geographic areas. In the present study, we developed a novel approach for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. This approach was applied to quantify and explain yield gaps in rainfed and irrigated soybean in the North-Central USA (NC USA) region, which accounts for about one third of soybean global production. Survey data on yield and management were collected from 3568 producer fields over two crop seasons and grouped into 10 technology extrapolation domains (TEDs) according to their soil, climate, and water regime. Yield potential was estimated using a combination of crop modeling and boundary functions for water productivity and compared against highest producer yields derived from the yield distribution in each TED-year. Yield gaps were calculated as the difference between yield potential and average producer yield. Explanatory factors for yield gaps were investigated by identifying management practices that were concordantly associated with high- and low-yield fields. Management × TED interactions were then evaluated to elucidate the underlying causes of yield gaps. The chosen spatial TED framework accounted for about half of the regional variation in producer yield within the NC USA region. Across the 10 TEDs, soybean average yield potential ranged from 3.3 to 5.3 Mg ha −1 for rainfed fields and from 5.3 to 5.6 Mg ha −1 for irrigated fields. Highest producer yields in each TED were similar (±12%) to the estimated yield potential. Yield gap, calculated as percentage of yield potential, was larger in rainfed (range: 15–28%) than in irrigated (range: 11–16%) soybean. Upscaled to the NC USA region, yield potential was 4.8 Mg ha −1 (rainfed) and 5.7 Mg ha −1 (irrigated), with a respective yield gap of 22 and 13% of yield potential. Sowing date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation in half or more of the 10 TEDs. However, the degree to which these three factors influenced producer yield varied across TEDs. Analysis of in-season weather helped interpret management × TED interactions. For example, yield increase due to advances in sowing date was greater in TEDs with less water limitation during the pod-setting phase. The present study highlights the strength of combining producer survey data with a spatial framework to measure yield gaps, identify management factors explaining these gaps, and understand the biophysical drivers influencing yield responses to crop management.

The current investigation tested the effectiveness of an online student research project designed to supplement traditional methods (e.g., lectures, discussions, and assigned readings) of teaching research methods in a large-enrollment Introduction to Psychology course. Over the course of the semester, students completed seven assignments, each representing a stage of the research process. Students formed hypotheses, tested their hypotheses using data from the class, interpreted their results, generated future directions, created PowerPoint slides summarizing their projects, and presented their results in a poster session. We found support for the hypothesis that the research methods intervention would lead to better performance on a research methods quiz compared to students in a nonintervention section taught by the same instructor. This intervention demonstrated that it is feasible to use project-oriented active-learning techniques to foster understanding of research methods in large classes.

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.

Sudden death syndrome (SDS), caused by Fusarium virguliforme, is an important yield limiting disease of soybean. Glyphosate is used to control weeds in soybean; however, its effect on SDS is not clearly understood. The objective of this study was to examine the impact of glyphosate on SDS, yield, and plant nutrition under field conditions. Fourteen field experiments were conducted in Iowa, Illinois, Indiana, Michigan, Wisconsin, and Ontario, Canada during 2011 to 2013. The experiment consisted of six treatment combinations of glyphosate and herbicides not containing glyphosate. Disease index was significantly different across the location–years, ranging from 0 to 65. The highest disease was noted in locations with irrigation, indicating that high soil moisture favors development of SDS. There were no effects of herbicide treatments or interactions on disease. The foliar disease index among the treatments over all years ranged from 9 to 13. Glyphosate-treatments also tended to yield more than treatments of herbicides not containing glyphosate. There were no interactions between glyphosate-treatments and total manganese in plant tissue. The interaction of glyphosate with other nutrients in plant tissue was inconclusive. This 14 location–year study demonstrated that glyphosate application did not increase SDS severity or adversely affect soybean yield under field conditions. UCR::Vicerrectoría de Docencia::Ciencias Agroalimentarias::Facultad de Ciencias Agroalimentarias::Escuela de Agronomía

Soybean [Glycine max (L.) Merr.] seed composition and yield are a function of genetics (G), environment (E), and management (M) practices, but contribution of each factor to seed composition and yield are not well understood. The goal of this synthesis-analysis was to identify the main effects of G, E, and M factors on seed composition (protein and oil concentration) and yield. The entire dataset (13,574 data points) consisted of 21 studies conducted across the United States (US) between 2002 and 2017 with varying treatments and all reporting seed yield and composition. Environment (E), defined as site-year, was the dominant factor accounting for more than 70% of the variation for both seed composition and yield. Of the crop management factors: (i) delayed planting date decreased oil concentration by 0.007 to 0.06% per delayed week (R 2∼0.70) and a 0.01 to 0.04 Mg ha-1 decline in seed yield per week, mainly in northern latitudes (40-45 N); (ii) crop rotation (corn-soybean) resulted in an overall positive impact for both seed composition and yield (1.60 Mg ha-1 positive yield difference relative to continuous soybean); and (iii) other management practices such as no-till, seed treatment, foliar nutrient application, and fungicide showed mixed results. Fertilizer N application in lower quantities (10-50 kg N ha-1) increased both oil and protein concentration, but seed yield was improved with rates above 100 kg N ha-1. At southern latitudes (30-35 N), trends of reduction in oil and increases in protein concentrations with later maturity groups (MG, from 3 to 7) was found. Continuing coordinated research is critical to advance our understanding of G × E × M interactions.

Crop simulation models are used at the field scale to estimate crop yield potential, optimize current management, and benchmark input-use efficiency. At issue is the ability of crop models to predict local and regional actual yield and total production without need of site-year specific calibration of internal parameters associated with fundamental physiological processes. In this study, a well-validated maize simulation model was used to estimate yield potential for 45 locations across the U.S. Corn Belt, including both irrigated and rainfed environments, during four years (2011–2014) that encompassed diverse weather conditions. Simulations were based on measured weather data, dominant soil properties, and key management practices at each location (including sowing date, hybrid maturity, and plant density). The same set of internal model parameters were used across all site-years. Simulated yields were upscaled from locations to larger spatial domains (county, agricultural district, state, and region), following a bottom-up approach based on a climate zone scheme and distribution of maize harvested area. Simulated yields were compared against actual yields reported at each spatial level, both in absolute terms as well as deviations from long-term averages. Similar comparisons were performed for total maize production, estimated as the product of simulated yields and official statistics on maize harvested area in each year. At county-level, the relationship between simulated and actual yield was better described by a curvilinear model, with decreasing agreement at higher yields (>12 Mg ha−1). Comparison of actual and simulated yield anomalies, as estimated from the yearly yield deviations from the long-term actual and simulated average yield, indicated a linear relationship at county-level. In both cases (absolute yields and yield anomalies comparisons), the agreement increased with increasing spatial aggregation (from county to region). An approach based on long-term actual and simulated yields and year-specific simulated yield allowed estimation of actual yield with a high degree of accuracy at county level (RMSE ≤ 18%), even in years with highly favorable weather or severe drought. Estimates of total production, which are of greatest interest to buyers and sellers in the market, were also in close agreement with actual production (RMSE ≤ 22%). The approach proposed here to estimate yield and production can complement other approaches that rely on surveys, field crop cuttings, and empirical statistical methods and serve as basis for in-season yield and production forecasts.

Planting date and cultivar selection are major factors in determining the yield potential of any crop and in any region. However, there is a knowledge gap in how climate scenarios affect these choices. To explore this gap, we performed a regional scale analysis (11 planting dates × 8 cultivars × 281 fields × 36 weather years × 6 climate scenarios) using the APSIM model and pSIMS software for Iowa, the leading US maize (Zea mays L.) producing state. Our objectives were to determine how the optimum planting date (optPD) changes with weather scenarios and cultivars and the potential economic implications of planting outside the optimum windows. Results indicated that the mean optPD corresponds to the US Department of Agriculture, National Agriculture Statistics Service (USDA-NASS) 18.4% planting progress (April 28th) in Iowa. The optPD was found to be advancing by –0.13 d yr−1 from 1980 to 2015. A 1 °C increase in mean temperature increased the length of the growing season by 10 days while the optPD changed by –2 to + 6 days, depending on cultivar. Under a more realistic scenario of increasing the minimum temperature by 0.5 °C, decreasing the maximum temperature by 0.5 °C, increasing spring rainfall by 10% and decreasing summer rainfall by 10%, the optPD only changed by –2 days compared to current trends, however, yield increased by 6.6%. Analysis of historical USDA-NASS planting durations indicated that on average, the planting duration (1–99% statewide reported planting progress) is 44 days, while it can be as low as 21 days in years with favorable weather. A simple economic analysis illustrated a potential revenue loss up to $340 million per year by planting maize outside the optimum window. We conclude that future investments in planting technologies to accelerate planting, especially in challenging weather years, as well as improved optPD × cultivar recommendations to farmers, will provide economic benefits and buffer climate variability.

In 2018, the Forecast and Assessment of Cropping sysTemS (FACTS) project was replicated to achieve the objective of forecasting and evaluating in-season soil-crop dynamics. This concept was initiated to help farmers and agronomists make in-season management decisions, plus identify management practices that could have been changed to improve grain yields, net profits, and also reduce environmental impacts.

Recommendations for soybean seeding rates have been targeted between 125,000 and 140,000 seeds/acre with the objective of targeting a final plant population of at least 100,000 plants/acre. It is understood a 15-in. row spacing has approximately 4.5 bushels/acre higher yields than 30-in. row spacing. Even though this advantage has been identified, the shift to 15-in. rows has not occurred. In recent years, 20-in. row spacing planters have become available. Therefore, this trial was designed to begin to look at seeding rate advantages in 20-in. rows compared with 15-in. and 30-in. row spacing.

Iowa landowners and farmers increasingly are seeing the value of single species cover crops. In theory, cover crop mixtures have the same advantages as diverse species ecosystems like prairies. The most important advantage would be greater and more stable total plant growth. Mixing species with complimentary features can take advantage of multiple niches and environmental conditions in space, weather, time, and seasons.

In 2018, the Forecasting and Assessment of Cropping sysTemS (FACTS) project continued 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, plus review the past growing season to see what management practices could have been changed to improve grain yields and net profits, but also reduce nitrogen loss.

Meeting demand for food, fiber, feed, and fuel in a world with 9.7 billion people by 2050 without negative environmental impact is the greatest scientific challenge facing humanity. We hypothesize that this challenge can only be met with current and emerging technologies if guided by proactive use of a broad array of relevant data and geospatial scaling approaches to ensure local to global relevance for setting research priorities and implementing agricultural systems responsive to real-time status of weather, soils, crops, and markets. Despite increasing availability of field-scale agricultural data, robust spatial frameworks are lacking to convert these data into actionable knowledge. This commentary article highlights this knowledge gap and calls attention to the need for developing robust spatial frameworks that allow appropriate scaling to larger spatial domains by discussing a recently developed example of a data-driven strategy for estimating yield gaps of agricultural systems. To fully leverage research on sustainable intensification of cropping systems and inform policy development at different scales, we call for new approaches combining the strengths of top-down and bottom-up approaches which will require coordinated efforts between field scientists, crop modelers, and geospatial researchers at an unprecedented level.