2016-11-03T14:11:40

The anticipated release of EnlistTM cotton, corn, and soybean cultivars likely will increase the use of 2,4-D, raising concerns over potential injury to susceptible cotton. An experiment was conducted at 12 locations over 2013 and 2014 to determine the impact of 2,4-D at rates simulating drift (2 g ae ha−1) and tank contamination (40 g ae ha−1) on cotton during six different growth stages. Growth stages at application included four leaf (4-lf), nine leaf (9-lf), first bloom (FB), FB + 2 wk, FB + 4 wk, and FB + 6 wk. Locations were grouped according to percent yield loss compared to the nontreated check (NTC), with group I having the least yield loss and group III having the most. Epinasty from 2,4-D was more pronounced with applications during vegetative growth stages. Importantly, yield loss did not correlate with visual symptomology, but more closely followed effects on boll number. The contamination rate at 9-lf, FB, or FB + 2 wk had the greatest effect across locations, reducing the number of bolls per plant when compared to the NTC, with no effect when applied at FB + 4 wk or later. A reduction of boll number was not detectable with the drift rate except in group III when applied at the FB stage. Yield was influenced by 2,4-D rate and stage of cotton growth. Over all locations, loss in yield of greater than 20% occurred at 5 of 12 locations when the drift rate was applied between 4-lf and FB + 2 wk (highest impact at FB). For the contamination rate, yield loss was observed at all 12 locations; averaged over these locations yield loss ranged from 7 to 66% across all growth stages. Results suggest the greatest yield impact from 2,4-D occurs between 9-lf and FB + 2 wk, and the level of impact is influenced by 2,4-D rate, crop growth stage, and environmental conditions.

Recent advances in remote sensing technology, especially in the area of Unmanned Aerial Vehicles (UAV) and Unmanned Aerial Systems (UASs) provide opportunities for turfgrass breeders to collect more comprehensive data during early stages of selection as well as in advanced trials. The goal of this study was to assess the use of UAV-based aerial imagery on replicated turfgrass field trials. Both visual (RGB) images and multispectral images were acquired with a small UAV platform on field trials of bermudagrass (Cynodon spp.) and zoysiagrass (Zoysia spp.) with plot sizes of 1.8 by 1.8 m and 0.9 by 0.9 m, respectively. Color indices and vegetation indices were calculated from the data extracted from UAV-based RGB images and multispectral images, respectively. Ground truth measurements including visual turfgrass quality, percent green cover, and normalized difference vegetation index (NDVI) were taken immediately following each UAV flight. Results from the study showed that ground-based NDVI can be predicted using UAV-based NDVI (R2 = 0.90, RMSE = 0.03). Ground percent green cover can be predicted using both UAV-based NDVI (R2 = 0.86, RMSE = 8.29) and visible atmospherically resistant index (VARI, R2 = 0.87, RMSE = 7.77), warranting the use of the more affordable RGB camera to estimate ground percent green cover. Out of the top ten entries identified using ground measurements, 92% (12 out of 13 in bermudagrass) and 80% (9 out of 11 in zoysiagrass) overlapped with those using UAV-based imagery. These results suggest that UAV-based high-resolution imagery is a reliable and powerful tool for assessing turfgrass performance during variety trials.

In this review, we examine opportunities and challenges for 21st-century robotic agricultural cotton harvesting research and commercial development. The paper reviews opportunities present in the agricultural robotics industry, and a detailed analysis is conducted for the cotton harvesting robot industry. The review is divided into four sections: (1) general agricultural robotic operations, where we check the current robotic technologies in agriculture; (2) opportunities and advances in related robotic harvesting fields, which is focused on investigating robotic harvesting technologies; (3) status and progress in cotton harvesting robot research, which concentrates on the current research and technology development in cotton harvesting robots; and (4) challenges in commercial deployment of agricultural robots, where challenges to commercializing and using these robots are reviewed. Conclusions are drawn about cotton harvesting robot research and the potential of multipurpose robotic operations in general. The development of multipurpose robots that can do multiple operations on different crops to increase the value of the robots is discussed. In each of the sections except the conclusion, the analysis is divided into four robotic system categories; mobility and steering, sensing and localization, path planning, and robotic manipulation.

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To address the effectiveness of predawn leaf water potential in plant‐based irrigation scheduling, Gossypium hirsutum L. plants were grown under fully irrigated and dryland conditions and under three predawn water potential (ΨPD) thresholds (−0.5, −0.7, and −0.9 MPa). Measurements included ΨPD, plant height, mainstem node number, lint yield, water productivity, and continuous crop canopy temperature. We found that ΨPD produced similar yields to current practices, while decreasing overall water use from 7 to 31%, depending on rainfall levels and the treatment utilized. When considered across both years of the study (2013 and 2014), using a −0.5‐MPa ΨPD irrigation threshold consistently resulted in less irrigation applied than the checkbook method and maximum water productivity and lint yield. Using a well‐watered baseline developed in 2013 for canopy temperature versus vapor pressure deficit, we calculated a crop water stress index (CWSI) that exhibited a very strong, nonlinear relationship with season average ΨPD values between approximately −0.4 and −0.7 MPa (r2 = 0.81). A strong, nonlinear relationship was also seen between CWSI and lint yield (r2 = 0.81). Predawn water potential appears to be an effective means of determining the need for irrigation in cotton, and in the current study, yield and water productivity were maximized at a season‐long average ΨPD threshold of −0.5 MPa. Furthermore, when calibrated using ΨPD–based irrigation triggers, canopy‐temperature derived CWSI appears to be a promising tool for future automated plant‐based irrigation scheduling in the southeastern United States.

Current planting technology possesses the ability to increase crop productivity and improve field efficiency by precisely metering and placing crop seeds. Planter performance depends on determining and utilizing optimal settings for different planting variables such as seed depth, down pressure, and seed metering unit. The evolution of "Big Data" in agriculture today brings focus on the need for quality as-planted and yield mapping data. Therefore, an investigation was conducted to evaluate the performance of current planting technology for accurate placement of seeds while understanding the accuracy of as-planted data. Two studies consisting of two different setups on a 6-row, John Deere planter for seeding of maize (Zea mays L.) were conducted. The first study aimed at assessing planter performance at 2 depth settings (25 and 51 mm) and four different down pressure settings (varying from none to high), while the second study focused on evaluating planter performance during variable-rate seeding with treatments consisting of two seed metering units (John Deere Standard and Precision Planting's eSet setups) with five different seeding rates and four ground speed treatments which provided a combination of 20 different meter speeds. Field data collection consisted of measuring plant emergence, plant population and seed depth whereas plant spacing, plant population after emergence along with distance and location for rate changes within the field were also recorded for the variable-rate seeding study. Results indicated that both depth setting and downforce affected final seeding depth. Measured seed depth was significantly different from the target depth even though time was spent adjusting the units to achieve the desired prior to planting. Crop emergence did not vary significantly for the different depth and downforce settings except for target depth in Field 1. Results from the variable-rate study indicated that seeding rate changes were accomplished within a quick response time (< 1 s) at all ground speeds regardless of magnitude of rate change. Data showed that planter performance in terms of emergence and plant spacing CV was comparable for most of the meter speeds (17.4-33.5 rpm) among the two seed meters utilized in the study. Plant spacing CV increased with an increase in meter speed, however no significant differences existed among meter speeds in the range of 17.4-33.5 rpm. Results implied that correct seed metering unit setup is very critical to obtain expected performance of today's planting technology. A concerning find was that the quality of as-applied maps from the commercial variable-rate display was not reflective of the actual planter performance in the field. The study recommended that operators need Extended author information available on the last page of the article Precision Agriculture (2020) 21:603-619 1 3 to ensure the correct planter and display setups in order to achieve needed seed placement performance to support variable-rate seeding.

This study addressed the potential of reduced prebloom irrigation, referred to as primed acclimation (PA), to increase agricultural water-use efficiency (WUE) using a soil-moisture-based irrigation scheduling system in cotton (Gossypium hirsutum L.). To address this, a study was conducted near Camilla, GA, under a variable-rate, center-pivot irrigation system using a Watermark-based automated soil moisture sensing approach to measure soil water potential (SWP) and impose varying irrigation scheduling treatments during the prebloom stage of development. Early season thresholds were −20 (Treatment 1), −40 (Treatment 2), −70 (Treatment 3), to −100 kPa (Treatment 4) prior to flowering. Reductions in prebloom irrigation of up to 17% were noted in this study for the driest thresholds (−100 kPa) with no reduction in lint yield relative to the −20 and −40 kPa thresholds. In some cases, prebloom irrigation was eliminated completely in the drier prebloom threshold treatments with no appreciable yield reductions. In rainfed treatments, episodic drought during flowering (2014) limited plant growth and node production, hastened cutout, decreased boll numbers per plant, produced a more compact boll distribution on the plant, and decreased yield and WUE relative to irrigated treatments. In contrast, season-long rainfed treatments exhibited the highest WUE in 2015 (a wet season). The results of this study conclude that prebloom irrigation thresholds between −70 and −100 kPa appear to be viable for use in a sensor-based PA irrigation scheduling system, whereas episodic drought coinciding with flowering represents a major threat to yield stability and WUE for cotton production in the southeastern United States.