Microspacecraft and Earth observation: Electrical field (ELF) measurement project

The Utah State University space system design project for 1989 to 1990 focuses on the design of a global electrical field sensing system to be deployed in a constellation of microspacecraft. The design includes the selection of the sensor and the design of the spacecraft, the sensor support subsystems, the launch vehicle interface structure, on board data storage and communications subsystems, and associated ground receiving stations. Optimization of satellite orbits and spacecraft attitude are critical to the overall mapping of the electrical field and, thus, are also included in the project. The spacecraft design incorporates a deployable sensor array (5 m booms) into a spinning oblate platform. Data is taken every 0.1 seconds by the electrical field sensors and stored on-board. An omni-directional antenna communicates with a ground station twice per day to down link the stored data. Wrap-around solar cells cover the exterior of the spacecraft to generate power. Nine Pegasus launches may be used to deploy fifty such satellites to orbits with inclinations greater than 45 deg. Piggyback deployment from other launch vehicles such as the DELTA 2 is also examined

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

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

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

Soybean nitrogen fixation dynamics in Iowa, USA

The rainfed US Midwestern region has deep, fertile soils and leads the US 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.This is a manuscript of an article published as Córdova, S. Carolina, Michael J. Castellano, Ranae Dietzel, Mark A. Licht, Kaitlin Togliatti, Rafael Martinez-Feria, and Sotirios V. Archontoulis. "Soybean nitrogen fixation dynamics in Iowa, USA." Field Crops Research 236 (2019): 165-176. doi:10.1016/j.fcr.2019.03.018. Posted with permission. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

Linking crop- and soil-based approaches to evaluate system nitrogen-use efficiency and tradeoffs

Increasing nitrogen (N)-use efficiency (NUE) is key to improving crop production while mitigating ecologically-damaging environmental N losses. Traditional approaches to assess NUE are principally focused on evaluating crop responses to N inputs, often consider only what happens during the growing season, and ignore other means to improve system efficiency, such as by tightening the cycling of soil N (e.g. with N scavenging cover crops). As the goals of improving production and environmental quality converge, new metrics that can simultaneously capture multiple aspects of system performance are needed. To fill this gap, we developed a theoretical framework that links both crop- and soil-based approaches to derive a system N-use efficiency (sNUE) index. This easily interpretable metric succinctly characterizes N cycling and facilitates comparison of systems that differ in biophysical controls on N dynamics. We demonstrated the application of this new approach and compared it to traditional NUE metrics using data generated with a process-based model (APSIM), trained and tested with experimental datasets (Iowa, USA). Modeling of maize-soybean rotations indicated that despite their high crop NUE, only 45% of N losses could be attributed to the inefficient use of N inputs, whereas the rest originated from the release of native soil N into the environment, due to the asynchrony between soil mineralization and crop uptake. Additionally, sNUE produced estimates of system efficiency that were more stable across weather years and less correlated to other metrics across distinct crop sequences and N fertilizer input levels. We also showed how sNUE allows for the examination of tradeoffs between N cycling and production performance, and thus has the potential to aid in the design of systems that better balance production and environmental outcomes.This is a manuscript of an article published as Martinez-Feria, Rafael A., Michael J. Castellano, Ranae N. Dietzel, Matt J. Helmers, Matt Liebman, Isaiah Huber, and Sotirios V. Archontoulis. "Linking crop-and soil-based approaches to evaluate system nitrogen-use efficiency and tradeoffs." Agriculture, Ecosystems & Environment 256 (2018): 131-143. doi:10.1016/j.agee.2018.01.002. Posted with permission.This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License

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

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 > 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 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.This article is published as Ordóñez, Raziel A., Michael J. Castellano, Jerry L. Hatfield, Matthew J. Helmers, Mark A. Licht, Matt Liebman, Ranae Dietzel et al. "Maize and soybean root front velocity and maximum depth in Iowa, USA." Field Crops Research 215 (2018): 122-131. doi: 10.1016/j.fcr.2017.09.003.</p

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.</p