Variable Application Research Articles

Droplet Deposition and Efficacy of Real-Time Variable-Rate Application of Herbicides at Reduced Dose in Winter Wheat Fields

Using an intelligent plant protection machine for spraying herbicides at a real-time variable rate plays a key role in improving the utilization efficiency of herbicides and reducing environmental pollution. Spraying volume (SV) and nozzle size (NS) are key factors influencing droplet deposition and herbicide efficacy and safety. A three-way split-split plot design experiment was conducted in the winter wheat field, with SV 180 L·ha−1 and 150 L·ha−1 in the main plot, a turbo air induction nozzle TTI11004 and TTI11003 in the subplot, herbicide flucarbazone-Na 70% WG mixed with florasulam 50 g·L−1 SC as the recommended dose, and a 20% reduced dose in the sub-subplot. Droplet deposition and weed control efficacy treated by these three factors and their combination were evaluated. Results indicated that there was a significant influence of SV on droplet coverage and density, but no significant influence of NS and its interaction with SV. A droplet coverage and density of treatment at 180 L·ha−1 were both significantly higher than at 150 L·ha−1. The influence of SV and its interaction with NS on weed control efficacy were significant. The efficacy of treatment TTI11004 at SV 180 L·ha−1 was the highest but decreased when NS was switched to TTI11003 and the SV was decreased to 150 L·ha−1. There was no significant effect of all the treatments on winter wheat yield and its components, but the yield loss could be reduced by 2.36% when the herbicide input was reduced by 20%. We can conclude that herbicide input can be reduced by at least 20% using the intelligent machine while equipped with the right NS at the right SV, which would increase the safety of winter wheat production.

Comprehensive study of on-the-go sensing and variable rate application of liquid nitrogenous fertilizer

Variable rate technology (VRT) provides a sustainable, efficient, and cost-effective nutrient application option. A pioneered study is carried out to develop a variable rate liquid fertilizer application system to detect on-the-go deficiency of Nitrogen within a field and apply it according to the requirement of crop. The system includes microcontroller, nitrogen sensor (i.e. Green-seeker sensor), proportional solenoid valve, pressure relief valve, pump, battery, etc. Microcontroller receives a signal from Green-seeker sensor and it transmits to the proportional solenoid valve (PSV). The PSV opening is change based on the prescribed nutritional dose. The experimental study is conducted on groundnut crop. The experiment is carried out for different level of fertilizer (N1-150, N2– 200, N3-250, and N4-300 l ha−1) as well as different operating speed is 3, 4, 5 km h−1 to estimate actual fertilizer dropped (AFD), % error, droplet size, droplet density (DD), uniformity coefficient (UC), and spray deposition. The response time of the system is in the range of 1.12 s to 1.80 s. It is observed from the experimental study that the error in the fertilizer to be dropped and AFD increases with increase in the speed of operation. The percentage error is found to be 2.73 %, which is the lowest one, at 3 km h−1 operating speed. The maximum droplet size is found to be 272.5 μm at 5 km h−1 forward speed and 300 l ha−1 nitrogen application rate, whereas minimum droplet size is found to be 241.23 μm at 3 km h−1 forward speed and 150 l ha−1 nitrogen application rate. The present study proposes maximum of 24.30 % saving in the fertilizer application for groundnut crop. Thus, the present system improved fertilizer use efficiency, profitability while minimizing environmental hazards.

Evaluation of Variable Application Rate of Fertilizers Based on Site-Specific Management Zones for Winter Wheat in Small-Scale Farming

China is currently experiencing a severe issue of excessive fertilization. Variable rate fertilization (VRF) technology is key to solving this issue in precision agriculture, and one way to implement VRF is through management zone (MZ) delineation. This study is aimed at evaluating the feasibility and potential benefits of VRF based on site-specific MZs in smallholder farm fields. This study determined the amounts of basal and top-dressing fertilizers in different spatial units, based on soil nutrient MZs and crop growth MZs, respectively. The potential agronomic, economic, and environmental advantages of spatial variable rate fertilization were further assessed by comparing the farmer’s treatment, the expert’s treatment, and the variable rate fertilization treatment based on management zones (VR-MZ). The results showed that VR-MZ reduced the use of nitrogen (N), phosphorus (P), and potassium (K) fertilizers by 22.90–43.95%, 59.11–100%, and 8.21–100%, respectively, and it also increased the use efficiency of N, P, and K by 12.27–28.71, 89.64–176.85, and 5.48–266.89 kg/kg, respectively, without yield loss. The net incomes of VR-MZ were 15.5–449.61 USD ha−1 higher than that of traditional spatially uniform rate fertilization. Meanwhile, less nitrous oxide emission (23.50–45.81%), ammonia volatilization (19.38–51.60%), and nitrate ion leaching amounts (28.77–53.98%) were found in VR-MZ compared to those in uniform fertilization. The results suggest that the VR-MZ has great potential for saving fertilizers, significantly increasing farmers’ net income, reducing environmental pollution, and promoting the sustainable use of resources. This study provides a theoretical basis and technical support for exploring a VRF suitable for village-scale farming.

Considering field topography in the model-based assessment of a centrifugal spreader’s variable rate application accuracy

In the application of solid mineral fertilizers with twin-disc spreaders, accurate delivery of the prescribed rates is highly dependent on their dynamic performance. Additional to the effect of imprecision in geolocations and the delayed reaction of the spreader to the rate updates at the delineations of the task map, the field surface with inclined hills and undulating waves significantly influences the field application accuracy. In this paper, the effect of the determined field topography on application accuracy has been assessed. Necessary sensors such as the prism of a Total Station and an IMU (Inertial Measurement Unit) have been attached to a mobile sled to acquire the geo-referenced positions together with its real-time tilting information while following the actual field profile. A model for the spreader performance on a flat field surface, which had been developed by the authors in previous work, was improved by incorporating the measured tilt information corresponding to the measured field conditions. In addition, the improved model used the measured topography with inclinations and undulations to determine the landing location of each fertilizer particle. The influence of the determined field inclinations and undulations on the stationary spreading was assessed by comparing the distributions of the fertilizers in the transversal direction that are extracted from the spreading on the flat and measured field surfaces. The comparative analysis of the distributions indicated a significantly high value of 27.8% for a root-mean-squared deviation. The subsequent assessment of the actual field topography’s effect on the spreader’s dynamic performance showed that the measured field surface conditions have a significant effect on the application accuracy in terms of absolute deviation, with approx. 5% under- and approx. 3% over-application of the maximum prescribed amount.

Management Soil Zones, Irrigation, and Fertigation Effects on Yield and Oil Content of Coriandrum sativum L. Using Precision Agriculture with Fuzzy k-Means Clustering

Precision agriculture (PA), management zone (MZ) strategies at the field level, soil analyses, deficit irrigation (DI), and fertilizer Variable Rate Application (VRA) are management strategies that help farmers improve crop production, fertilizer use efficiency, and irrigation water use efficiency (IWUE). In order to further investigate these management strategies, the effects of four soil MZ treatments, which were delineated using PA with fuzzy k-means clustering, two irrigation levels [IR1:FI = full drip irrigation (>90% of θfc), IR2:VDI = variable deficit drip irrigation (60–75% of θfc)], and four VRA fertilizations were studied on coriander yield and essential oil content in a two-year research project in Greece. A daily soil-water-crop-atmosphere (SWCA) balance model and a daily depletion model were developed using sensor measurements (climatic parameter sensors as well as soil moisture sensors). Unbalanced one-way ANOVA (p = 0.05) statistical analysis results revealed that correct delineation of MZs by PA with fuzzy k-means clustering, if applied under deficit irrigation and VRA fertilization, leads to increased essential oil content of coriander with statistically significant differences (SSD) and lower fruit yields; however, without SSD differences among management zones, when appropriate VRA fertilization is applied to leverage soil nutrient levels through the different fuzzy clustered MZs for farming sustainability. Moreover, VDI compared to full irrigation in different MZs yields 22.85% to 29.44% in water savings, thus raising IWUE (up to 64.112 kg m−3), nitrogen efficiency (up to 5.623), and N-P-K fertilizer productivity (up to 5.329).

Development of algorithm for variable nitrogen rate application in cotton crop by implementing tractor mounted nitrogen sensor

To produce cotton, growers must regulate nitrogen (N) during the growing season, which is one of the most essential considerations. Any new methods, techniques, or technologies that might help farmers regulate N, a crucial choice in crop productivity, will be of great use to them. In the present study, by utilizing tractor-mounted nitrogen sensor (TMNS) technology, field experiments were conducted to design an algorithm for variable nitrogen rate application for cotton crop varieties V1 (NCS 855 BG-II) and V2 (RCH 773 BG-II). Under node formation, fruit squaring, squaring, early, mid, peak flowering, and maturity crop growth stages, relationships between the attributes of TMNS and variable nitrogen application rate (VNAR) were also created for V1 and V2. The coefficient of determination between NDVI and N content (%) in leaves was 0.933 and 0.711 at the mid-flowering stage for varieties V1 and V2, respectively. Both varieties differ significantly in their N response, with mid-flowering (76 DAS) being a crucial growth period. So, algorithms were developed for converting sensor sufficiency index values to N application rates for V1 and V2 at the mid-flowering stage. For cultivars V1 and V2, the crucial sensor-based sufficiency index thresholds for initiating VNAR were 0.960 and 0.860, respectively. The developed mid-blooming phase algorithms were useful to predict mid-season N-application rates for cotton crops.

Within-field spatial variability and potential for profitability of variable rate applications

Within-field spatial variability and potential for profitability of variable rate applications

Conservation intensification under risk: An assessment of adoption, additionality, and farmer preferences

AbstractConservation practices used on agricultural cropland can provide important ecosystem services. The United States relies largely on voluntary programs to incentivize adoption of conservation practices, but the success of these efforts relies on good program design. We use a choice experiment to evaluate farmers' willingness to adopt more intensive in‐field conservation practices (continuous no‐till, conservation crop rotation, cover crops, and variable rate application of inputs) using a nonlinear extended expected utility framework that incorporates risk under a conservation contract. We contribute to the literature by providing insights regarding producers' preference for incentive payment mechanism (federal program or carbon market) and the incentive payment needed to induce additionality and practice continuity. We find that although an additional payment might be needed to promote practice continuity among farmers who have previously adopted cover crops, in the case of continuous no‐till, adopters might be willing to forgo additional payment to continue benefiting from the use of this practice. Our results also revealed producers prefer conservation contracts accruing higher off‐farm environmental benefits and federally administered over carbon market programs. This is an important result given an increased public interest in climate change and new proposed carbon mitigation policies and private carbon market initiatives.

Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation

Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact.

Remote-Sensing-Based Sampling Design and Prescription Mapping for Soil Acidity

Soil acidification is a major problem in the inland Pacific Northwest. A potential solution is the application of lime to neutralize acidity and raise pH. As lime is an expensive input, precision variable rate application is necessary. However, high-resolution mapping of pH and buffer pH for lime prescription requires costly sampling and analysis. To reduce the amount of sampling needed, remote sensing, which correlates with soil pH and buffer pH, can be used to determine optimal sampling locations and allow optimal interpolation. We used soil and crop data from the Washington State University R.J. Cook Agronomy Farm to develop an optimal sampling plan for a farmer’s property, followed that sampling design, and used the measured pH and buffer pH to fit a Bayesian hierarchical model using remote sensing variables specific to that farmer’s land. Following this, a new model was developed for the research farm with similar covariates. Ultimately, on the farmer’s field, we observed a root mean square error (RMSE) of 0.2487 for soil pH at a depth of 0–10 cm and 0.1221 for modified Mehlich buffer at 0–10 cm of depth. For the research farm, where buffer pH was not measured, we saw an RMSE of 0.3272 for soil pH at 0–10 cm of depth and 0.3381 for soil pH at 10–20 cm of depth. The ability make predictions of soil acidity with uncertainty using this technique allows for prescription lime application while optimizing soil sampling and testing. Further, this paper serves as a case study of on-farm research.

Lettuce modelling for growth control in precision agriculture

Improving the efficiency of agriculture is a growing priority due to food security issues, environmental concerns, and economics. Precision agriculture and variable rate application technology could enable increases in yield while maintaining or reducing fertiliser use. However, this requires the development of control algorithms which are suitable for the challenges of agriculture. In this paper, we propose a new mechanistic open model of lettuce growth for use in control of precision agriculture. We demonstrate that our model is cooperative and fits well to experimental data. We use the model to show, via simulations, that a simple proportional distributed control law increases crop uniformity and yield without increasing nitrogen use, even in the presence of sparse actuation and noisy observations.

Toward an effective approach for on-farm experimentation: lessons learned from a case study of fertilizer application optimization in Japan

On-farm experimentation (OFE) is increasing worldwide. Appropriate OFE procedures may differ depending on the characteristics and circumstances surrounding farms, such as climate, field conditions, farm size, degree of agricultural digitalization, and a farmer’s socioeconomic background. This study aims to guide the future development of OFE in Japanese grain farming by examining the experimental setup, data analysis, and farmers’ activities within their socioeconomic and institutional communication and learning networks. The results of this typical OFE case study, which estimates a field’s economically-optimal fertilizer variable-rate application map for winter wheat production, are reported. The outcomes of the case study, which are intended to guide the direction of OFE development in Japan, were used as reference materials for a survey taken while interviewing farmers who had never been involved in OFE. Farmers’ answers showed that the economic return of site-specific management depends on farm and field size and exhibits economies of scale. A very high share of the profit increases provided by OFE data came from improvements in field-specific uniform rate management, not from within-field site-specific management. The interviews revealed that farmers open to OFE are more interested in increasing rice crop quality to earn price premiums than in increasing yield. Increased engagement with farmers in conducting OFEs could play a key role not only in generating data to guide farmers’ input management but also in fostering farmer collaboration to develop marketing strategies. This study is the first to propose future orientations of OFE research that target typical moderately-sized Japanese grain farms.

Can crop modelling, proximal sensing and variable rate application techniques be integrated to support in-season nitrogen fertilizer decisions? An application in corn

Nitrogen (N) fertilization in corn is often based on uniform rates and yield goals without considering the spatial and temporal variability of yield potential. It is well documented how uniform N rates lead to low N use efficiencies and environmental issues, resulting in reduced profit for farmers. Several site-specific approaches have been proposed to capture the yield spatial variability and adjust N rates to the actual crop nutrient requirements. The current study presents two original, site-specific N fertilization approaches, where two approaches at integrating crop simulation models, seasonal forecast and proximal sensing were tested across two corn seasons (2019 and 2020) in a field with significant spatial variability. In the first approach, top dressing N prescription maps were determined using the DSSAT crop model run with historical weather data, while in the second one, the maps were determined coupling DSSAT with seasonal forecasts and proximal sensing. Compared to the uniform fertilization treatment, both model-based approaches led to higher yields, N efficiency and gross margin in 2019 but not in 2020. The 2020 season was characterized by several major rainfall events, which were not present in the historical or seasonal forecast datasets. This inconsistency led to a substantial underestimation of the N leaching events in both model-based methodologies and consequently to higher-than-needed N fertilizer recommendations. Future studies should therefore focus on identifying ways to provide accurate seasonal estimates of extreme weather events to enable crop models to provide better N recommendations. In addition, the integration of proximal and remote sensing data into the crop model should be tested later in the season when spatial variability in crop N status peaks.

Zonal Application of Plant Growth Regulator in Cotton to Reduce Variability and Increase Yield in a Highly Variable Field

Variable-rate application has great potential to reduce variability and increase yield by spatially optimizing agricultural inputs. In cotton, plant growth regulators (PGRs) control excessive growth and provide suitable plant height for harvest operations. This study evaluates the effect of variable-rate PGR application compared to constant-rate application to reduce yield spatial variability and increase yield. The variable-rate approach was carried out in 2020 based on zonal applications defined by clustering analysis using soil electrical conductivity, vegetation indexes, and yield maps. Application doses and timings were determined by integrating plant height measurements for the whole field in 2019 and by zone in 2020. To compare the two procedures, cultivar and plant populations were kept constant; fertilization and accumulated rain were similar in both seasons. A reduction in yield spatial variability due to the zonal application was observed, with yield coefficient of variation (CV) decreasing from 18% in 2019 to 12% in 2020. Spatial and temporal analysis of Normalized Difference Vegetation Index satellite images showed higher CV values in 2019 (constant-rate) reaching 30% at the end of the season, whereas in 2020 (variable-rate) CV was constant (approximately 10%). Cotton yield increased from 3.5 to 4.3 t ha-1 between 2019 and 2020, which can be partially attributed to the variable-rate approach. The variable-rate approach based on application zones and plant height measurements was a viable strategy for reducing yield spatial variability and likely increasing yield in a highly variable cotton field.

A farm-scale sustainability assessment of the anaerobic digestate application methods

Digestate is the anaerobic digestion by-product that can be used as an organic fertilizer, but some agronomic and environmental concerns still hinder its application on land. This study aims to evaluate the agronomic performances (i.e., silage maize dry yield, protein content, and N uptake) and environmental sustainability of solid and liquid digestate fraction application in a field experiment involving two farms with different textures covering about 47 ha in North Italy. The best available distribution methods included mineral fertilizer (MF), mineral fertilizer in variable rate application (VRA) (VRA-MF), liquid digestate with a nitrification inhibitor (LD+), liquid digestate in VRA (VRA-LD), liquid digestate with a nitrification inhibitor in VRA (VRA-LD+), and solid digestate (SD) and were applied to silage maize (Zea mays L.) in 2019 and 2020 cropping seasons. Results showed that both digestate fractions gave satisfying agronomic performances (i.e., dry biomass > 13 t ha-1 and protein content > 6.8%), comparable to those of mineral fertilizers, irrespective of soil type and application techniques. On the contrary, system sustainability investigated with a spatial evaluation of nitrogen use efficiency (NUE) revealed a strong interaction between NUE and soil texture. Indeed, in fine-textured soil only the adoption of both VRA and the nitrification inhibitor allowed the liquid digestate to reach a NUE between 50% and 90% while SD exhibited poor NUE (e.g., < 50%). In conclusion, liquid digestate fraction might be an effective substitute for mineral fertilizers in silty soils meeting also environmental criteria when VRA or nitrification inhibitors are applied. Contrarily, longer-term experiments are requested to evaluate SD fraction sustainability.

Assessment of map based variable rate strategies for copper reduction in hedge vineyards

Reduction of Plant Protection Products (PPP) has become a priority in agriculture, led by European directives and regulations due to the negative impacts and social awareness that pesticides have raised in the population. In viticulture, the use of copper to control Downy Mildew (Plasmopara viticola) is posing severe problems of soil, water and environment contamination, and European Commission is regulating its use through the EU regulation 2018/1981. A targeted spraying management can be done benefiting from the novel development of variable rate application (VRA) technology for orchard sprayers, resulting in savings of PPP. In this research, the quality of the application in terms of copper deposit in leaves of two VRA strategies where compared versus the conventional strategy performed by the farmer (REF). In each of the two VRA strategies, copper dosage was selected 1) maintaining copper dose per hectare (VRA) and 2) following the copper concentration listed in the product label (VRA[Cu]). Drone based NDVI maps from three commercial vineyards were used to characterize the structure of the vegetation in every vigour zone and manual measures in field were used to determine the most appropriate spraying volume rate according to canopy characteristics.Results show that a reduction of 33 and 44 % of copper used per hectare did not have significant impact on the quality of the application. On the contrary, while leaf deposit was comparable to that achieved by REF strategy, the product was better distributed across the entire canopy. Savings of 40 % of copper could be achieved in in the PPP application calendar along the season 2019 with no influence in disease control. Impact of PPP can be reduced by incorporating digital technologies, and the objectives of the Farm to Fork strategy and the European Green Deal can be met ensuring a more sustainable agri-food production.

Parameter Calibration and Systematic Test of a Discrete Element Model (DEM) for Compound Fertilizer Particles in a Mechanized Variable-Rate Application

In order to obtain accurate discrete element simulation model (DEM) parameters of compound fertilizer and solve the problem of challenging measurement of contact parameters of compound fertilizer particle, simulation calibration test was carried out by using EDEM simulation soft-ware. This study measured the intrinsic parameters and contact parameters of compound fertilizer particles through physical tests and established a simulation model that corresponds with the actual situation to calibrate the contact parameters of compound fertilizer particles. By using the Blackett-Burman test, the parameters that had a significant impact on the compound fertilizer’s resting angle were determined by the fertilizer-fertilizer collision recovery coefficient, fertilizer-fertilizer rolling friction coefficient, and fertilizer-steel static friction coefficient. Utilizing the steepest ascent test, the ideal value intervals for the three key parameters were identified. Box-Burman response surface analysis was used to establish and optimize the regression model of the rest angle and significant parameters. With the actual rest angle as the target value, the best combination of significant parameters was found, which was used for the simulation verification test. The simulated rest angle was 20.61°, whereas the real rest angle was 19.95°, with a relative error of 3.31%. The results demonstrate that the calibration parameters are essentially accurate representations of the real characteristics, which can serve as a reference point for simulation research and optimization design of variable fertilizer spreader.

Improving the congruency of satellite-based fertilizer maps with field-operable units using pneumatic spreaders

The dramatically increasing nitrogen fertiliser prices and growing environmental awareness emphasise the need to optimise machine operability to avoid double overlapping of the target dose rate in different field units, including headlands and field bodies, to be more congruent with satellite-based nitrogen prescription maps. However, the map grids frequently do not correspond with farmers’ management practices, specifically machine operability. To this end, this study develops an algorithm that subdivides any given field into operable units by considering pneumatic spreaders based on the actual tramline on the field and placing the operable units along with this. The newly developed algorithm allows ex-ante specification of the required fertiliser amount. It shows how well such techniques can implement an optimised application map supported by the statistical information for each field unit. Uniform fertilizer application can further be compared with variable rate application (VRA) as well as machinery with and without section control, allowing the identification of areas with the greatest deviation and the excess amount of nitrogen applied. A more precise nitrogen application on both homogeneous or heterogeneous field units saves nitrogen and decreases the negative environmental impact.

Precision fruticulture in Flanders – a four-year study on sensing techniques, data analysis and variable rate applications

Precision fruticulture in Flanders – a four-year study on sensing techniques, data analysis and variable rate applications

Application of Precision Agriculture for the Sustainable Management of Fertilization in Olive Groves

Olive tree growing (Olea europaea L.) has considerably increased in the last decades, as has the consumption of extra virgin olive oil in the world. Precision agriculture is increasingly being applied in olive orchards as a new method to manage agronomic variability with the aim of providing individual plants with the right input amount, limiting waste or excess. The objective of this study was to develop a methodology on a GIS platform using GEOBIA algorithms in order to build prescription maps for variable rate (VRT) nitrogen fertilizers application in an olive orchard. The fertilization plan was determined for each tree by applying its own nitrogen balance, taking into account the variability of nitrogen in soil, leaf, production, and actual biometric and spectral conditions. Each olive tree was georeferenced using the S7-G Stonex instrument with real-time kinematic RTK positioning correction and the trunk cross section area (TCSA) was measured. Soil and leaves were sampled to study nutrient variability. Soil and plant samples were analyzed for all major physical and chemical properties. Spectral data were obtained using a multispectral camera (DJI multispectral) carried by an unmanned aerial vehicle (UAV) platform (DJI Phantom4). The biometric characteristics of the plants were extracted from the achieved normalized vegetation index (NDVI) map. The obtained prescription map can be used for variable rate fertilization with a tractor and fertilizer spreader connected via the ISOBUS system. Using the proposed methodology, the variable rate application of nitrogen fertilizer resulted in a 31% reduction in the amount to be applied in the olive orchard compared to the standard dose.