Corrigendum to “Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems” [Field Crops Res. 273 (2021) 108264]

Field Crops Research · 2022-01-07

Assessing the impacts of cover crops on maize and soybean yield in the U.S. Midwestern agroecosystems

Field Crops Research · 2021 · 93 citations

• Agronomy
• Agroforestry
• Environmental science

Design of on‐farm precision experiments to estimate site‐specific crop responses

Agronomy Journal · 2020 · 10 citations

• Computer Science
• Statistics
• Mathematics

Abstract Site‐specific prescriptions require estimating response functions to controllable inputs across the field. The methodology of applying geographically weighted regression to on‐farm precision experimentation studies opens new opportunities to study site‐specific responses to inputs in farmers' fields by locally estimating the regression coefficients. However, the effect of the experiment's spatial layout, such as plot dimensions and randomization, and spatial structure of the yield response on the experiment performance are yet to be studied. Detailed information about these effects is needed to improve trial design to detect site‐specific responses. A simulation study was conducted using 14,400 fields of 37 ha and 9‐m resolution. Coefficients from a spatial variable response function were drawn from five random fields generated by unconditional Gaussian geostatistical simulations. Four levels of nitrogen were assigned to plots using 18 systematic and randomized chessboard designs with different plot sizes. Simulated yield data was obtained by combining the coefficients, treatment, and random error. The effect of spatial structure and the designs was assessed with measures of agreement between the true and estimated maps of regression coefficients. The ability to capture or approximate the true spatial pattern of the response function increased as the underlying response function's spatial structure increases. Overall differences in performance between design were observed across the spatial structure tested, mostly related to randomization and plot dimensions. In general best results were achieved by systematic designs with small or intermediate plot sizes ( r = 0.54 ± 0.05, MAE = 0.005 ± 0.0005, SDR = 0.81 ± 0.06, and CP = 0.50 ± 0.04). Our methodology provides a path for testing designs under different spatial variability scenarios.

Field‐specific yield response to variable seeding depth of corn in the Midwest

Agrosystems Geosciences & Environment · 2020 · 15 citations

• Environmental science
• Agronomy
• Soil science

Abstract Sufficient soil moisture is crucial for corn ( Zea mays L.) germination and emergence. As within‐field soil moisture varies, it is often expected that corn seeding depth should vary accordingly. As seedbeds get drier, deeper planting increases the chances of higher soil moisture and faster emergence. The goal was to evaluate the corn yield response to shallow and deep seeding depths compared with the standard seeding depth while making use of management zones delineated using relative elevation data as surrogate of spatial patterns of soil moisture. We hypothesize that crop yield responds positively to shallow seeding depth in zones with low relative elevation values, wet zones, whereas the opposite would be expected in drier zones in a range of locations across the U.S. Midwest. Landscape position (LSP) values (i.e., relative elevation values) were computed from LIDAR data and used to approximate the spatial soil moisture distribution by splitting variability into dry, transitional, and wet LSP zones. Field‐long strips were planted in 17 commercial fields in 2014 and 2015 at shallow, standard, and deep seeding depths. The LSP zones were a significant predictor of the yield response to shallow or deep seeding depth only in 5 and 2 out of 17 field‐years, respectively. Significant overall responses of yield to shallow or deep seeding depth were found in 6 and 8 out of 17 field‐years, respectively. The yield response to variable seeding depth of corn showed high field‐specificity and was likely attenuated by favorable conditions for corn planting and during the growing seasons.

Experimental Designs and Estimation Methods for On‐Farm Research: A Simulation Study of Corn Yields at Field Scale

Agronomy Journal · 2019-11-01 · 24 citations

On‐farm experimentation using Precision Agriculture technology enables farmers to make decisions based on data from their fields. Results from on‐farm experiments depend on the experimental design and statistical analyses performed. Detailed information about the accuracy of the treatment effect estimates, and Type I error rates of hypothesis testing under different spatial structure scenarios attained by alternative experimental designs and analysis is required to improve on‐farm research experiments. Three thousand yield data sets were drawn from 15 random fields simulated by unconditional Gaussian geostatistical simulation technique and were modeled by applying 10 experimental designs and three estimation methods with experimental units ranging from 138 to 9969 m 2 . No effect of spatial structure, experimental design, and estimation methods was observed on overall mean yield and treatment bias. Unaddressed changes of nugget/sill ratio and range of variogram had a significant effect on estimator efficiency and accuracy with Type I error rates above the nominal rate, which increased with higher spatial autocorrelation. Spatial methods were robust to changes in spatial structure regardless of the design. Randomization of treatment increased the uncertainty of model estimators. In general, the accuracy of treatment effect estimates increased with the number of replications of smaller size. The opposite trend was observed between those estimates and the size of the plots. Analyses showed that the best designs for testing the overall treatment effect in two‐treatment experiments would be split‐planter, strip‐plots, and chessboard because of their size and number of experimental units. Core Ideas Spatial autocorrelation increases grand mean estimator variance in any design or method. Spatial autocorrelation reduces treatment effect estimator efficiency if not modeled. Spatial autocorrelation increases Type I error if not modeled. Designs with small experimental units (strip plots or chessboard) performed better.

Reply to: Brazilian ethanol expansion subject to limitations

Nature Climate Change · 2019-02-25 · 11 citations

Evaluación de diseños experimentales y métodos de análisis para ensayos en campos de productores con herramientas de agricultura de precisión

El Servicio de Difusión de la Creación Intelectual (National University of La Plata) · 2018-09-01

Los costos de realizar experimentos en campos de productores se ha reducido con el desarrollo de agricultura de precisión. No obstante, es necesario evaluar los diseños comúnmente utilizados considerando la variabilidad espacial y autocorrelación de los datos. Se evaluaron diseños experimentales sistemáticos simulando condiciones experimentales a campo con distinta estructura de autocorrelación espacial. Los diseños se diferenciaron en la eficiencia de los estimadores como resultado de su sensibilidad al efecto de la estructura espacial. En general, el grado de de estructura espacial (proporción varianza nugget) disminuyó la precisión de los estimadores y aumentó la tasa de error simulada tipo I, siendo mayor el efecto en diseños con parcelas grandes. El diseño en tablero de ajedrez mostró mejor performance general. El método de estimación GLS (con parámetros fijos) mejoró las propiedades de los estimadores obtenidos.

Sequential Pyroxasulfone Applications with Fluxofenim Reduce Sorghum Injury and Increase Weed Control

Agronomy Journal · 2018-08-09 · 15 citations

Core Ideas Selective control of weedy grasses is difficult in grain sorghum due to the presence of wild relatives. Pyroxasulfone provided greater weed control than S ‐metolachlor but caused more crop injury. Sorghum protection from pyroxasulfone provided by fluxofenim was dependent on the environment. Split applications of pyroxasulfone equal to the highest single rate provided similar weed control. Controlling weeds selectively is a challenge when producing grain sorghum [ Sorghum bicolor (L.) Moench]. Pyroxasulfone, a preemergence (PRE) herbicide, has demonstrated excellent grass and broadleaf control in maize ( Zea mays L.) and soybean [ Glycine max (L.) Merr]. However, pyroxasulfone is not labeled for grain sorghum because crop injury is a major limitation. Our first objective was to evaluate five herbicide safeners in the greenhouse to determine their ability to protect sorghum from pyroxasulfone. Growth data indicated seed‐applied fluxofenim provided the highest level of protection to emerging seedlings. A second objective was to evaluate fluxofenim for protecting sorghum from single and sequential pyroxasulfone applications in the field. A split‐plot in a randomized complete block design evaluated six pyroxasulfone (whole plot) and two fluxofenim treatments (subplot) in 2015 and 2016. A single PRE treatment of S ‐metolachlor, an untreated‐weedy control, and weed‐free control were compared with pyroxasulfone to assess weed control, crop injury and stand count, and grain yield. Pyroxasulfone provided greater weed control than S ‐metolachlor. However, as pyroxasulfone rates increased both weed control and crop injury increased, regardless of safener. In contrast, sequential pyroxasulfone applications (90/120 or 120/90 g ai ha −1 ) did not elicit as much crop injury or stand reductions as a single PRE application at the same total rate (210 g ai ha −1 ) and maintained weed control, which resulted in higher yields. Despite increased crop tolerance and yield with sequential relative to single pyroxasulfone applications, these findings indicate a more effective herbicide safener for pyroxasulfone in grain sorghum is required.

Brazilian sugarcane ethanol as an expandable green alternative to crude oil use

Nature Climate Change · 2017-10-23 · 203 citations

Establishing Switchgrass with a Corn Companion Crop to Improve Economic Profitability

Agronomy Journal · 2016-01-04 · 2 citations

Establishing switchgrass ( Panicum virgatum L.) usually takes 2 yr, and revenue is not typically generated from the land until the end of the second year. Much is already known about establishing switchgrass as a bioenergy or forage crop, but identifying additional methods of establishment that provide revenue during the planting year without negatively impacting long‐term stand density or biomass yield would better incentivize farmers to convert portions of land to growing switchgrass. A field experiment was conducted to evaluate the impact of companion corn ( Zea mays L.) seeding rate (49, 59, and 69 ×10 3 seeds ha −1 ) and establishment year N fertilizer rate (0, 112, and 224 kg N ha −1 ) on corn yield, switchgrass stand density, second‐ and third‐year biomass production, and the net economic return over the 3‐yr establishment period. Switchgrass stand densities averaged 5 plants m −2 fewer when companion cropped with corn compared with switchgrass planted alone (25.9 plants m −2 ), but all treatments resulted in successful stands with at least 17 plants m −2 . Net annual economic return over the 3‐yr establishment period was maximized under most price scenarios when switchgrass was companion cropped with corn fertilized with 112 or 224 kg N ha −1 during the establishment year. These results confirm that switchgrass can be established with corn to improve short‐term net economic returns without negatively affecting long‐term switchgrass stand health.