Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery

Accurate crop yield estimation is important for agronomic and economic decision-making. This study evaluated the performance of imagery data acquired using a unmanned aerial vehicle (UAV)-based imaging system for estimating yield of maize (Zea mays L.) and the effects of variable-rate nitrogen (N) application on crops. Images of a 27-ha maize field were captured using a UAV with a consumer-grade RGB camera flying at ~100 m above ground level at three maize growth stages. The collected sequential images were stitched and the Excess Green (ExG) colour feature was extracted to develop prediction models for maize yield and to examine the effect of the variable-rate N application. Various linear regression models between ExG and maize yield were developed for three sample area sizes (21, 106, and 1058 m2). The model performance was evaluated using coefficient of determination (R2), F-test and the mean absolute percentage error (MAPE) between estimated and actual yield. All linear regression models between ExG and yield were significant (p ≤ 0.05). The MAPE ranged from 6.2 to 15.1% at the three sample sizes, although R2 values were all \u3c0.5. Prediction error was lower at the later growth stages, as the crop approached maturity, and at the largest sample level. The ExG image feature showed potential for evaluating the effect of variable-rate N application on crop growth. Overall, the low-cost UAV imaging system provided useful information for field management

Early corn stand count of different cropping systems using UAV-imagery and deep learning

Optimum plant stand density and uniformity is vital in order to maximize corn (Zea mays L.) yield potential. Assessment of stand density can occur shortly after seedlings begin to emerge, allowing for timely replant decisions. The conventional methods for evaluating an early plant stand rely on manual measurement and visual observation, which are time consuming, subjective because of the small sampling areas used, and unable to capture field-scale spatial variability. This study aimed to evaluate the feasibility of an unmanned aerial vehicle (UAV)-based imaging system for estimating early corn stand count in three cropping systems (CS) with different tillage and crop rotation practices. A UAV equipped with an on-board RGB camera was used to collect imagery of corn seedlings (~14 days after planting) of CS, i.e., minimum-till corn-soybean rotation (MTCS), no-till corn-soybean rotation (NTCS), and no-till corn-corn rotation with cover crop implementation (NTCC). An image processing workflow based on a deep learning (DL) model, U-Net, was developed for plant segmentation and stand count estimation. Results showed that the DL model performed best in segmenting seedlings in MTCS, followed by NTCS and NTCC. Similarly, accuracy for stand count estimation was highest in MTCS (R2 = 0.95), followed by NTCS (0.94) and NTCC (0.92). Differences by CS were related to amount and distribution of soil surface residue cover, with increasing residue generally reducing the performance of the proposed method in stand count estimation. Thus, the feasibility of using UAV imagery and DL modeling for estimating early corn stand count is qualified influenced by soil and crop management practices

Estimating corn emergence date using UAV-based imagery

Assessing corn (Zea Mays L.) emergence uniformity soon after planting is important for relating to grain production and for making replanting decisions. Unmanned aerial vehicle (UAV) imagery has been used for determining corn densities at vegetative growth stage 2 (V2) and later, but not as a tool for detecting emergence date. The objective of this study was to estimate days after corn emergence (DAE) using UAV imagery. A field experiment was designed with four planting depths to obtain a range of corn emergence dates. UAV imagery was collected during the first, second and third weeks after emergence. Acquisition height was approximately 5m above ground level resulted in a ground sampling distance 1.5 mm pixel-1. Seedling size and shape features derived from UAV imagery were used for DAE classification based on the Random Forest machine learning model. Results showed image features were distinguishable for different DAE (single day) within the first week after initial corn emergence with a moderate overall classification accuracy of 0.49. However, for the second week and beyond the overall classification accuracy diminished (0.20 to 0.35). When estimating DAE within a three-day window (± 1 DAE), overall 3-day classification accuracies ranged from 0.54 to 0.88. Diameter, area, and major axis length/area were important image features to predict corn DAE. Findings demonstrated that UAV imagery can detect newly-emerged corn plants and estimate their emergence date to assist in establishing emergence uniformity. Additional studies are needed for fine-tuning image collection procedures and image feature identification in order to improve accuracy

Environmental Implications of Increased Bioenergy Production on Midwest Soil Landscapes [abstract]

Only abstract of poster available.Track III: Energy InfrastructurePrairie soil landscapes encompass over 16 million acres in Missouri and surrounding states. Much of this area has been degraded by erosion but is still used for grain production. Erosion has caused variable topsoil depth within fields which in turn has resulted in greater within-field variability of crop yield, magnified the drought-prone nature of these soils, and lowered the overall soil productivity and ecosystem function. In recent years, pressure on these sensitive soils has risen due to higher demand for grain production, in part for ethanol and biodiesel. In some areas, highly erodible fields which were historically managed as CRP and pasture are being turned into grain crop acres. Thus as new and fluctuating feed and bioenergy markets develop, land management practices will also shift, resulting in changes in soil and water quality of watersheds. This presentation will explore the likely environmental implications of different types of bioenergy production on the soil resource. Further, the positive benefits of potential changes in land use will be in explored. For example, one alternative for sensitive soils is production of perennial grass as a feedstock for coal co-burning plants and for potential future use in cellulosic ethanol production. Perennial grass yields are likely to be less variable than grain yields, both year-to-year and within fields, primarily because of greater resistance to drought. Grass production systems also provide environmental services not obtained from annual grain crops. We will also discuss our work on developing ways to target the most appropriate places in the landscape for grain or perennial production so as to enhance ecosystem services and improve soil and water quality

Role of inherent soil characteristics in assessing soil health across Missouri

Soil health indicator values vary based on parent material, native vegetation, and other soil forming factors; therefore, useful interpretations require consideration of inherent soil characteristics. Our objective was to evaluate the distribution of soil health indicators across soil and climate gradients throughout the state of Missouri through a statewide cover crop cost-share program. Soil samples (0–7 cm) were collected from 5,300 agricultural fields and analyzed for several soil health indicators. Comparisons were made among six regions in the state based on Major Land Resource Area and county boundaries. Results varied for soil organic carbon (C), active C, potentially mineralizable nitrogen, water stable aggregates, and cation exchange capacity by region and corresponded with soil forming factors. Interpretation of soil health indicators must account for regional factors, recognizing that areas with different inherent values have a different potential for soil health

A long‑term precision agriculture system sustains grain profitability

After two decades of availability of grain yield-mapping technology, long-term trends in field-scale profitability for precision agriculture (PA) systems and conservation practices can now be assessed. Field-scale profitability of a conventional or ‘business-as-usual’ system with an annual corn (Zea mays L.)-soybean (Glycine max [L.]) rotation and annual tillage was assessed for 11 years on a 36 ha field in central Missouri during 1993 to 2003. Following this, a ‘precision agriculture system’ (PAS) with conservation practices was implemented for the next 11 years to address production, profit and environmental concerns. The PAS was multifaceted and temporally dynamic. It included no-till, cover crops, crop rotation changes, site-specific N and variable-rate or zonal P, K and lime. Following a recent evaluation of differences in yield and yield variability, this research compared profitability of the two systems. Results indicated that PAS sustained profits in the majority (97%) of the field without subsidies for cover crops or payments for enhanced environmental protection. Profit was only lower with PAS in a drainage channel where no-till sometimes hindered soybean stands and wet soils caused wheat (Triticum aestivum L.) disease. Although profit gains were not realized after 11 years of PA and conservation practices, this system sustained profits. These results should help growers gain confidence that PA and conservation practices will be successful

A new perspective when examining maize fertilizer nitrogen use efficiency, incrementally

For maize (Zea mays L.), nitrogen (N) fertilizer use is often summarized from field to global scales using average N use efficiency (NUE). But expressing NUE as averages is misleading because grain increase to added N diminishes near optimal yield. Thus, environmental risks increase as economic benefits decrease. Here, we use empirical datasets obtained in North America of maize grain yield response to N fertilizer (n = 189) to create and interpret incremental NUE (iNUE), or the change in NUE with change in N fertilization. We show for those last units of N applied to reach economic optimal N rate (EONR) iNUE for N removed with the grain is only about 6%. Conversely stated, for those last units of N applied over 90% is either lost to the environment during the growing season, remains as inorganic soil N that too may be lost after the growing season, or has been captured within maize stover and roots or soil organic matter pools. Results also showed iNUE decrease averaged 0.63% for medium-textured soils and 0.37% for fine-textured soils, attributable to fine-textured soils being more predisposed to denitrification and/or lower mineralization. Further analysis demonstrated the critical nature growing season water amount and distribution has on iNUE. Conditions with too much rainfall and/or uneven rainfall produced low iNUE. Producers realize this from experience, and it is uncertain weather that largely drives insurance fertilizer additions. Nitrogen fertilization creating low iNUE is environmentally problematic. Our results show that with modest sub-EONR fertilization and minor forgone profit, average NUE improvements of ~10% can be realized. Further, examining iNUE creates unique perspective and ideas for how to improve N fertilizer management tools, educational programs, and public policies and regulations

Cropping system and landscape characteristics influence long-term grain crop profitability

Converting from standard tillage or no-tillage cropping systems to more conservation-based cropping systems that include no-tillage, cover crops, and reduced agrichemical inputs must be profitable for large-scale adoption. Therefore, research was conducted at the central Mississippi River Basin site of the USDA Long-Term Agroecosystem Research Network from 1996 to 2009 to determine how cropping systems, landscape position, and depth to claypan affected net economic return. Treatments consisted of three cropping systems {mulch-till corn (Zea mays L.)–soybean [Glycine max (L.) Merr.], MTCS; no-till corn–soybean, NTCS; no-till corn–soybean–wheat (Triticum aestivum L.) (NTCSW)–cover crop} and three landscape positions (summit, backslope, and footslope). Within each cropping system, landscape position influenced the depth to claypan and net returns, which were greatest in the summit and footslope positions. Across landscape positions, net return for NTCS was US$252 and$119 ha−1 yr−1 greater than MTCS and NTCSW, respectively. Net return of corn in MTCS and NTCSW was negative, whereas corn in NTCS averaged $97 ha−1 yr−1. Only NTCS corn exhibited a positive linear response in net return to depth to claypan. Soybean was much more profitable than corn, and both NTCS and NTCSW soybean were less influenced by landscape position and had at least$252 ha−1 yr−1 greater return than did MTCS soybean across landscape position. Results suggest that converting from MTCS to NTCS would have large positive impacts on reducing within-field variability and increasing profitability in the region, and modifications to the NTCSW system are needed to improve profitability

Data from a Public–industry Partnership for Enhancing corn Nitrogen Research

Improving corn (Zeamays L.) N managementis pertinent to economic andenvironmental objectives. However, there are limited comprehensive data sources to develop and test N fertilizer decision aid tools across a wide geographic range of soil and weather scenarios. Therefore, a public-industry partnership was formed to conduct standardized corn N rate response field studies throughout the U.S. Midwest. This research was conducted using a standardized protocol at 49 site-years across eight states over the 2014–2016 growing seasons with many soil, plant, and weather related measurements. This note provides the data (found in supplemental files), outlines the data, summarizes key findings, and highlights the strengths and weakness for those who wish to use this dataset