The use of cover crop-based rotational tillage has been proposed as a strategy to reduce tillage and control weeds in organic systems. Termination is a critical part of this system, as incomplete termination of the cover crop can lead to competition for moisture and nutrients with the commodity crop, poor weed suppression, and seed contamination of subsequent crops. The effect of different mechanical termination strategies for cereal rye (Secale cereale L.) and seeding rate on soybean [Glycine max (L.) Merr.] population and yield, weed biomass, and volunteer cereal rye growth was assessed. Factorial experiments were conducted on research stations and commercial farms across Wisconsin in 2019, 2020, and 2021, totaling 9-site years. Soybeans were planted without tillage at rates of 457,000 and 556,000 seeds ha-1 on 76 cm row widths into a cereal rye cover crop that was terminated using one of two commercially available roller crimpers or a sickle bar mower. While both crimping and mowing were found to be effective termination strategies, mowing increased weed biomass while decreasing soybean yields in some site years. Across 7-site years with the full factorial design (seeding rate was excluded as a factor in 2-site years), the mowed termination strategy led to 15% lower yields compared to one of the crimpers. This suggests mowing as a termination strategy is likely to be riskier under conditions of low cereal rye biomass and increasing grass weed pressure.

Reducing tillage in organic grains offers a path toward sustainable soil management. Living mulches provide a strategy for tillage reductions in organic corn ( Zea mays L.) that suppresses weeds and increases system biodiversity. Yet, organically approved management strategies need to be identified that minimize living mulch competition with commodity crops. An experiment conducted at the University of Wisconsin's Arlington Agricultural Research Station from 2021 to 2022 examined both pre‐ and post‐corn planting management strategies of a red clover ( Trifolium pratense L.) living mulch on clover growth, weed biomass, corn plant population, and grain yield. A 2 × 2 × 3 factorial experiment explored (1) pre‐plant tillage strategy comparing undercutting of clover versus no‐till management, (2) post‐plant flaming of clover versus no flaming, and (3) in‐season mechanical clover suppression strategies examining post‐plant use of inter‐row roller crimping and high‐residue cultivation used either alone or in combination. While corn yields remained relatively low across the experiment, averaging 4.61 Mg ha −1 , both flaming and undercutting showed promise as mechanical management strategies to improve corn yield. Undercutting increased corn yields over no‐tilling by 3.15 Mg ha −1 in 2021, while inadequate clover suppression resulted in no statistical differences in 2022. Inter‐row roller crimping reduced early‐season clover heights but did not affect end of season clover biomass. Clover biomass between rows was reduced using high‐residue cultivation; however, neither in‐season clover suppression strategy influenced corn yield. While yield gaps remain, mechanical management strategies suggest viability in improving corn yields in organic living mulch systems but require further exploration.

Concerns over tillage intensity and soil erosion in organic cropping systems have prompted the development of reduced tillage organic systems that use cover crops rather than soil disturbance as the primary weed control tool. However, planting into high residue cover crop mulches may inhibit crop establishment due to poor seed placement. Adaptation of agricultural equipment to high residue planting conditions could reduce variability in crop stands, thereby improving grain yield. Field trials at the University of Wisconsin Arlington Agricultural Research Station, Marshfield Agricultural Research Station, and three on-farm locations representing seven site-years of data from 2019 to 2020 comprised a 2 x 2 x 2 x 2 factorial experiment comparing (1) a low (457,135 seeds ha-1) versus high (555,986 seeds ha-1) soybean [Glycine max (L.) Merr.] seeding rate, (2) a 13 fluted-wavy coulter versus no coulter attachment, (3) low (667 N) versus high (1334 N) row unit down force, and (4) spiked versus rubber closing wheels on soybean stand establishment and grain yield in an organic cover crop-based reduced tillage system. Combined across site-years, high row unit down force and coulters increased soybean stand establishment by 2.3% and 2.4%, respectively; however, the magnitude and direction of effects varied within each site-year. Winter rye (Secale cereale L.) biomass accumulation negatively correlated to soybean stand establishment and demonstrated trends toward increasing soybean yield. Significant correlations between soybean stand establishment and yield were noted under high rye biomass conditions, indicating potential improvements to soybean yields by increasing plant stands. Rye biomass mulch accumulation reduces soybean stand establishment. Row unit down force of 1334 N and a coulter most consistently increased soybean stand establishment. Improvements in soybean plant stands translated to higher grain yields under elevated rye biomass conditions.

Cover crop residue retention on the soil surface can suppress weeds and improve organic no-till soybean (Glycine max) yield and profitability compared to a tilled system. Appropriate cereal rye (Secale cereale) fall planting date and termination methods in the spring are critical to achieve these benefits. A plot-scale agronomic experiment was carried out from September 2018 to October 2021 in Kutztown, PA, USA to demonstrate the influ-ence of cereal rye planting date (September or October) and mechanical termination method [no-till (I & J roller-crimper, Dawn ZRX roller, and mow-ted) and tilled (plow-cultivate)] on cover crop regrowth density, weed biomass, soybean yield, and economic returns. In one out of three years, the September rye planting accumulated more cover crop biomass than the October planting, but the regrowth of the rye after roller-crimping was greater with this plant-ing date. Cover crop planting date had no effect on total weed biomass and demonstrated varying effects on soybean grain yield and economic returns. The Dawn ZRX roller outper-formed the I & J roller-crimper in effectively terminating cover crops, while the I & J roller-crimper demonstrated more uniform weed suppression and led to greater soybean yields over a span of three years. Organic no-till strategies eliminated the need for tillage and reduced variable costs by 14% over plow-cultivated plots, and generated similar to 19% greater net revenue across the study period (no-till vs tillage = US $845 vs US $711 ha-1). Terminating cereal rye with roller-crimping technology can be a positive investment in an organic soybean production system.

Whole Plant Corn Silage (WPCS), produced by Self-Propelled Forage Harvesters (SPFH), is among the most common dietary ingredients for dairy cows. Kernel processor settings at harvest can impact WPCS quality and milk production. Having the capability to monitor machine performance and WPCS quality during harvest would allow farmers, nutritionists, and custom harvesters to optimize feed quality as the WPCS enters storage. A real -time quality assessment system was developed -a device to acquire images of chopped and processed silage moving through the spout of a SPFH and an image analysis algorithm to estimate the Kernel Processing Score (KPS) given a set of images. The system represents automation of the quality assessment process and could be incorporated in a real-time machine parameter tuning system. An initial experiment of corn particle detection in high-resolution images of stationary WPCS resulted in good detection performance (precision of 0.7381, recall of 0.9624, and intersection-over-union of 0.7104) but exposed inadequacies in the image labeling procedure and was not able to accurately estimate KPS. An improved camera system was designed and consisted of specialized exposure components to capture images at 121 frames per second. This system was able to acquire pictures silage moving at a rate of 36.9 m s-1. The KPS estimation was done offline and occurred in two steps: whole undamaged kernels were counted in a set of 1,000 images using machine learning; KPS was then estimated using a linear regression model previously obtained with the comparison between mechanical sieving KPS and number of kernels per image (r(38) = 0.967, p < 0.0001). A comparison between the estimated score to mechanically sieved KPS showed high correlation (r(38) = 0.977, p < 0.0001) and standard error of 2.71 %. Considering the computing power of high-end GPUs, the method is restrained by its current image acquisition rate, that results in one estimate in <10 s.

Cleaning yield monitor observations to remove erroneous points can improve the accuracy of yield estimates used for farm record keeping or on-farm research data collection, but current practices are time-intensive and cumbersome. cleanRfield is an open-source R package to improve the efficiency of processing spatial agricultural data such as yield maps. Compared with current standard yield monitor data cleaning solutions, cleanRfield can read and interpret a broader range of input data formats. Other key features of cleanRfield include automatic field boundary delineation and batch processing of data from multiple fields. In this Scientific Note, we overview functions within the cleanRfield package and introduce an integrative pipeline to evaluate and visualize yield monitor data. The package is being distributed under the GNU General Public License 2, and a more detailed tutorial including downloading instructions is available at .

The survey instrument that was used in the study to assess technology adoption among agricultural service providers.

Alfalfa is a valuable and widely adapted forage crop, and its nutritive value directly affects animal performance and ultimately affects the profitability of livestock production. Traditional nutritive value measurement method is labor-intensive and time-consuming and thus hinders the determination of alfalfa nutritive values over large fields. The adoption of unmanned aerial vehicles (UAVs) facilitates the generation of images with high spatial and temporal resolutions for field-level agricultural research. Additionally, compared with other imaging modalities, hyperspectral data usually consist of hundreds of narrow spectral bands and allow the accurate detection, identification, and quantification of crop quality. Although various machine-learning methods have been developed for alfalfa quality prediction, they were all single-task models that learned independently for each quality trait and failed to utilize the underlying relatedness between each task. Inspired by the idea of multitask learning (MTL), this study aims to develop an approach that simultaneously predicts multiple quality traits. The algorithm first extracts shared information through a long short-term memory (LSTM)-based common hidden layer. To enhance the model flexibility, it is then divided into multiple branches, each containing the same or different number of task-specific fully connected hidden layers. Through comparison with multiple mainstream single-task machine-learning models, the effectiveness of the model is illustrated based on the measured alfalfa quality data and multitemporal UAV-based hyperspectral imagery.

Adopting no-till planting can have many soil health benefits and cost savings but may present challenges with respect to planter performance. Aftermarket attachments such as hydraulic row unit down force and closing wheels have been hypothesized to overcome the challenges of uneven planting depth and poor furrow closure due to residue within the field. In addition, starter fertilizer could improve early season growth in cooler soil conditions. This study evaluated the effect of closing wheel type, row unit down force, and starter fertilizer on corn (Zea mays L.) emergence and yield across soil types and micro-climates within Wisconsin. Aftermarket closing wheels were compared to a standard (rubber) factory provided one, within two down force settings (Low at 333 N and High at 667 N) and liquid starter fertilizer (0 and 47 L ha(-1) of 7-21-7). The standard rubber closing wheel with high down force resulted in lower emergence compared to the low setting (p = .013). No effect of starter fertilizer was observed on either emergence or yield. Aftermarket wheels resulted in greater emergence regardless of down force setting, which might appear to be an advantage from an operator point of view, but greater corn emergence did not result in greater yield. These and other factors should be considered when selecting closing wheels for a particular set of seeding conditions.

Baling is an accepted process for densifying and transporting herbaceous biomass. Prior to transporting bales to an outlet for utilization, producers often group or stack the bales at the edge of the field. This is a tedious and labor-intensive task. Also, bale retrieval is time-sensitive to avoid losing the dry matter or damaging crop regrowth. In this work we developed an unmanned aerial vehicle (UAV) system for operational mapping and surveying of fields with bales waiting for removal. To that end, a commercial UAV (Inspire 2 with Zenmuse XS4 camera) was utilized to determine bale locations via the onboard vision system and the low-accuracy Global Navigation Satellite System (GNSS). Also bale locations were estimated by ground survey utilizing an RTK-corrected GNSS signal, in order to determine the bale locating accuracy of the developed system. Seven fields were investigated, in which various number of bales had been produced (from 9 bales up to 58 bales) in corn stover and soybean stubble. Based on this work we were able to make the following observations. Field orthomosaic images generated with and without ground control points yielded localization accuracies that were practically similar. Hence, a localization error of less than 0.4 m was observed when aerial and ground surveys taking place in the same day. Results also showed that when flights occurred in the same day, the precision error was low (0.019-0.142 m for different fields). Different flight velocities and aircraft altitudes maintained an accuracy within practical limits of detecting bales while improving the field capacity up to 91% through the increase in flight velocity and 184% through doubling flight altitude. The results of this work could be used by both manned and robotic bale collectors for in-field navigation and bale retrieval.

Cranberry fruitworm (Acrobasis vaccinii Riley (Lepidoptera: Pyralidae)) and blackheaded fireworm (Rhopobota naevana Hubner (Lepidoptera:Tortricidae)) threaten cranberry production annually by causing significant fruit damage. Up to four pesticide applications are made each year to control these insects, which are costly to producers and elevate pesticide residues in fruit. Pheromone-based mating disruption technology can provide control of these pests in cranberry production, with the potential to minimize, or eliminate, pesticide applications. In 2016, an uncrewed aerial vehicle (UAV) was investigated to apply a thick paraffin emulsion containing insect sex pheromones. Traditional agricultural equipment is not capable of applying the paraffin emulsion to cranberry beds due to the product's viscous, paste-like consistency. The first objective of this study was to retrofit an UAV (octocopter) with a novel extrusion device that had been engineered to deliver the pheromone-loaded paraffin at regular intervals during flight. The second objective was to confirm adequate distribution of the pheromones by measuring the mating disruption efficacy by monitoring male moth trap catches. The UAV was able to fly autonomously along a prescribed itinerary, deploying the paraffin product uniformly; however, the increased mass of the retrofitted UAV limited flight times to -12 min. The number of male cranberry fruitworm and blackheaded fireworm moths caught in lure-baited traps were reduced in the paraffin-treated beds compared with untreated beds, indicating adequate distribution of the pheromones. The UAV-applied pheromones concept could be developed into a production scale application method in the future, although issues of battery life and lifting capacity will need to be resolved.

Losses from the combine corn header result in decreased yield and profit. The development of improved corn headers to reduce losses is hampered by lack of sufficient tools for kernel loss assessment. A loss assessment system was developed that consisted of a residue clearing process to expose lost corn kernels on the ground, and a machine vision image system to quantify the exposed kernels. A mower deck was used to size-reduce and remove residue with minimal kernel displacement. The vision system consisted of an optical system for imaging the ground area and an image analysis program to identify lost kernels. The image analysis corn kernel detection system achieved an average precision of 0.90. A further assessment of system accuracy using random images from additional field tests resulted in an accuracy of 0.91. The combined residue clearing and machine vision systems achieved an overall system accuracy of 0.82 in field tests evaluating staged losses using known quantities of kernels. The loss analysis system was able to distinguish statistically significant (P < 0.05) differences in losses created by different corn header deck plate spacing, while requiring less time and labor than conventional assessment methods.

Highlights Aftermarket closing wheels increased corn emergence by 2% over standard rubber wheels. Yield was not significant by closing wheel type. Abstract . Producers are increasingly adopting cover crops and no-till planting for a variety of reasons including improving soil fertility and reducing energy inputs. However, adopting these practices may require changes in equipment and management strategy; therefore, research is needed to develop best practices for producers to reduce the risk and encourage adoption. The use of aftermarket closing wheels has been cited as a method to improve emergence under no-till conditions as preparing an ideal seedbed can be more difficult under these conditions due to limited seed-soil contact and side wall compaction. The effect of three aftermarket and the standard rubber closing wheels on emergence and yield under no-till planting of corn into heavy crop residue or cover crops was measured at three Wisconsin locations using a randomized complete block experimental design. Soil temperature and moisture was also monitored during the growing season. Corn plant emergence was measured at least three times to estimate the rate of emergence as a function of growing degree units using air and soil temperatures. The final emergence of corn planted with an aftermarket wheel was found to be significantly higher than the standard rubber closing wheel (p=0.069, a=0.1) across all locations. Yield was not found to be significant by wheel type most likely due to differences in field history and in season management practices. Keywords: Closing wheel, Cover crop, Emergence, No-till, Planter set-up.

Alfalfa is a valuable and intensively produced forage crop in the United States, and the timely estimation of its yield can inform precision management decisions. However, traditional yield assessment approaches are laborious and time-consuming, and thus hinder the acquisition of timely information at the field scale. Recently, unmanned aerial vehicles (UAVs) have gained significant attention in precision agriculture due to their efficiency in data acquisition. In addition, compared with other imaging modalities, hyperspectral data can offer higher spectral fidelity for constructing narrow-band vegetation indices which are of great importance in yield modeling. In this study, we performed an in-season alfalfa yield prediction using UAV-based hyperspectral images. Specifically, we firstly extracted a large number of hyperspectral indices from the original data and performed a feature selection to reduce the data dimensionality. Then, an ensemble machine learning model was developed by combining three widely used base learners including random forest (RF), support vector regression (SVR) and K-nearest neighbors (KNN). The model performance was evaluated on experimental fields in Wisconsin. Our results showed that the ensemble model outperformed all the base learners and a coefficient of determination (R-2) of 0.874 was achieved when using the selected features. In addition, we also evaluated the model adaptability on different machinery compaction treatments, and the results further demonstrate the efficacy of the proposed ensemble model.

Objective: Corn silage processing score (CSPS) is a well-known and often-used indicator of starch availability in whole-plant corn silage. However, obtaining results from a laboratory can take days or more. The objective of this work was to test an image-analysis method as a tool for quantitative assessment of corn kernel particle size and feed quality during harvest. Materials and Methods: Kernel processor gap settings of 1, 2, 3, and 4 mm were assessed using the standard sieving method and with an image-analysis method. In situ slowly disappearing DM in ruminally cannulated lactating dairy cows was also assessed at the various kernel processor gap settings and compared with the 2 CSPS estimation methods: image analysis and sieving. Results and Discussion: The image-analysis method was able to statistically separate mean estimated CSPS (P = 0.014) across the different crop processor gap settings for fresh samples. Image analysis CSPS estimation of fresh samples was highly correlated with in situ DM disappearance results [r(10) = 0.77] using a 12-h incubation time. These results indicate that image processing is a. viable tool for estimating CSPS and feed quality. Implications and Applications: A smartphone application, SilageSnap, was developed that uses the imageprocessing algorithm to estimate CSPS. This novel tool provides in-field estimation of CSPS that translates into actionable information regarding feed quality to inform machine adjustment decisions, which farmers, dairy nutritionists, and custom harvesters have not had in the past.

A comprehensive survey was developed and mailed to 3000 Wisconsin dairy (1000), livestock (500) and crop (1500) producers in early 2018. The survey was designed to evaluate internet and other digital technology use; digital technology applications deployed on farms; internet and mobile service satisfaction; barriers to technology adoption; and, use differences across groups based on demographic factors that included farm income, farm size, age, education level and gender. After eliminating undelivered/returned mailings, the survey had a 43% response rate (n = 1021). Internet access and use levels were strongly and positively associated with gender (women), farm income, farm size (hectares and numbers of animals) and education level. Higher levels of income, education, and acreage, females and ages under 55 were associated with significantly higher levels of internet access. For both mobile and home/office computer access, cost was an area with higher dissatisfaction along with slow download speed during heavy use periods. Weather and market information were categories of information most often accessed, while farm program and educational information were the categories least accessed. The most significant barriers to adoption of digital technology on respondents’ farms included data privacy and security concerns, software and system compatibility, and understanding how to use and derive value from acquired data. The survey examined areas of digital application adoption including finance and marketing tools and apps, precision planting and harvesting, sensor applications (soil, livestock, structures/environment), and robotic milking equipment and found adoption to increase with acreage, income, youth, and for females. In addition, a case study of two Wisconsin producers quantified and characterized all data generated on their farms in 2017. These producers stored 51 MB per hectare and 73 MB per milking head in 2017. These data provide insight into rural broadband needs that would allow agricultural producers to make data-driven decisions and foster innovation.

An image processing algorithm was developed to characterize the size distribution of corn kernel particles from Whole Plant Corn Silage (WPCS). The algorithm determines particle cross sectional area and maximum inscribed circle diameter as well as cumulative undersize percent, dimensions of significance, and key characteristics of the distribution including mean particle size, skewness, and kurtosis. Kernel Possessing Score (KPS) was derived from the area-weighted cumulative undersize percent of 4.75 mm. Samples of WPCS harvested using self-propelled forage harvester with crop processing roll gap clearance of 1, 2, 3, and 4 mm were analyzed in their fresh, dry, and sieved states. Algorithm results were compared with the standard method of mechanical sieving and found to be well correlated r(23) = 0.8, p < 0.001. Additionally, analysis of particles before and after sieving indicated that sieving significantly increased KPS for 1 (t(5) = 6.6, p = 0.001), 2 (t(5) = 4.2, p = 0.01), and 3 (t(5) = 3.5, p = 0.02) mm samples. This algorithm has the potential to more accurately determine KPS in field compared to currently available methods, allowing for adjustment of kernel processing during harvest which will improve silage quality.

The design and operation of ventilation systems for animal housing is a crucial component in maintaining a suitable environment for both animals and workers by removing heat, moisture, and gas species. However, important design and operational criteria, such as profiles of velocity and extent of mixing within animal housing, can be difficult to study experimentally. Thus, a computational fluid dynamics (CFD) model of a commercial dairy holding area, including the generation of transport of heat and gas species within the domain, was developed. The animal-occupied zone (AOZ) was modeled using porous media. The model was evaluated with experimental data of velocity and gas concentration. Results indicated that the airflow uniformity and air speed could be increased within the holding area by modifying the configuration of the side curtain openings and using concrete walls to guide airflow; a 20% increase in average velocity within the AOZ was achieved. Results of the CFD model have shown it to be an effective tool for gaining insight into the complex mixing patterns within holding areas to improve the design of animal housing.

Forage harvest is a time and energy intensive process requiring the coordination of multiple pieces of equipment. Detailed characterizations of the time spent in each work state for each piece of equipment would increase the understanding of process inefficiencies and aid in development of optimization tools. Geospatial and controller area network (CAN) machine data were recorded on forage harvesters and transport equipment, during two types of harvest operations, to quantify utilization of harvesters and transports as well as transport productivity. The data collection and processing method was successful in identifying work states for forage harvesters and transports. The results indicated that overall utilization of the harvester for harvesting was 61% and dependent on transport availability. The portion of total operational time spent in the idle work state (idle utilization) was 10% to 20% for transports and 18% to 23% for harvesters. A new metric for transport productivity was developed and found to be highly dependent on transport capacity ranging from 125 to 49 Mg km h(-1) for semi-trucks and smaller transports, respectively. The proposed data collection methods and productivity metrics could be used to optimize the forage harvest process to reduce idle time and maintain crop quality.

As agricultural producers continue to adopt precision management, big data applications, and “Internet of things” they are generating increasingly vast quantities of data on their operations. The collection and storage of extensive amounts of data is required to understand trends and make decisions. In addition, much of this data must transferred over the internet between locations on the operation, individuals, software, storage location, etc. Thus, broadband speed and bandwidth can limit productivity when time sensitive data must be transferred. The data was categorized by the activity in which it was generated, its intended use, and need to be transferred over the internet. This information will be used to better define rural broadband needs that would allow agricultural producers to make data-driven decisions and foster innovation. A pilot assessment of two Wisconsin producers found that in 2017, producers generated and stored 11 MB per hectare and 73 MB per milking head.