Variable Rate Nitrogen Fertilizer Research Articles

Estimation of Sensor-based site specific variable rate fertilizer application for maize (Zea mays L.) crop

Optical spectrometry sensors in crops offer a remarkable technological breakthrough in the field of variable-rate nitrogen fertilization. A field study was conducted during rainy (kharif) season of 2021 at the research farm of the Agricultural Engineering College and Research Institute Tamil Nadu Agricultural University Coimbatore to estimate maize crop nitrogen (N), Normalized Difference Vegetation Index (NDVI) value and chlorophyll content in hybrid maize COH (M) 8. Fertilizers were administered to the plots following the recommendations (250:75:75 kg NPK ha-1) given under Soil Test Crop Response, with a goal yield of 9t ha-1 predicted based on the initial soil available N, P, and K values. The experimental findings revealed a significant impact of nitrogen rate (P<0.001) on the percentage of nitrogen content in the leaves (% N leaf content). Additionally, there was a decrease in maize leaf chlorophyll content index over time, with ranges of 32.96 to 50.57, 28.78 to 41.78, 24.81 to 35.86, 22.12 to 28.54, and 14.34 to 20.56. On the contrary, the NDVI experienced an increase throughout the season, with ranges of 0.32 to 0.49, 0.30 to 0.55, 0.28 to 0.66, 0.46 to 0.88, and 0.56 to 0.84. The study will help foster sustainability within modern intensive farming practices by emphasizing the importance of reducing environmental pollution caused by applying Sensor-based site-specific nitrogen fertilizer for maize crop.

Economic feasibility of optical sensors for variable rate nitrogen fertilization in corn

Economic feasibility of optical sensors for variable rate nitrogen fertilization in corn

Evaluation of different crop model-based approaches for variable rate nitrogen fertilization in winter wheat

Several remote sensing-based methods have been developed to apply site-specific nitrogen (N) fertilization in crops. They consider spatial and temporal variability in the soil-plant-atmosphere continuum to modulate N applications to the actual crop nutrient status and requirements. However, deriving fertilizer N recommendations exclusively from remote proximal and remote sensing data can lead to substantial inaccuracies and new, more complex approaches are needed.Therefore, this study presents an improved approach that integrates crop modelling, proximal sensing and forecasts weather data to manage site-specific N fertilization in winter wheat. This improved approach is based on four successive steps: (1) optimal N supply is estimated through the DSSAT crop model informed with a combination of observed and forecast weather data; (2) actual crop N uptake is estimated using proximal sensing; (3) N prescription maps are created merging crop model and proximal sensing information, considering also the contribution of the soil N mineralisation; (4) N-Variable Rate Application (N-VRA) is implemented in the field. A VRA method based on DSSAT fed with historical weather data and a business-as- usual uniform fertilization were also compared.The methods were implemented in a 23.4 ha field in Northern Italy, cropped to wheat and characterized by large soil variability in texture and organic matter content. Results indicated that the model-based approaches consistently led to higher yields, agronomic efficiencies and gross margins than the uniform N application rate. Furthermore, the proximal sensing-based approach allowed capturing of the spatial variability in crop N uptake and led to a substantial reduction of the spatial variability in yield and protein content. This study grounds the development of web-based software as a friendly tool to optimize the N variable rate application in winter cereals.

Economic impacts of variable rate nitrogen fertilization: Input saving and yield increase in cereals

Abstract The purpose of this study is to discuss the economic impacts stemming from variable-rate (VR) nitrogen fertilization, one of the main precision agriculture practices. Economic impacts are related to reduced nitrogen use and increased crop yields thanks to a better distribution of inputs, net from possible spatial and temporal uncertainty. This paper reviews N=31 case studies (in 29 articles) that compared the performance of VR nitrogen applications versus uniform treatments. The comparisons covered nitrogen fertilization for cereal crops, in particular wheat and maize, for different years and in different countries. Findings highlight relevant changes in amounts of nitrogen applied, with the evidence of higher nitrogen efficiency resulting in reduced operating costs, while changes in crop yields are less evident: VR applications and uniform applications substantially reach the same production level, and higher nitrogen use efficiencies are achievable without significantly compromising yields. Overall, net economic impacts are in favour of VR fertilization. This work can raise farmers’ and other stakeholders’ knowledge of the actual economic impacts stemming from the adoption of VR fertilization and helps policymakers to understand the economic impact of precision agriculture and the need to foster sustainability-based policies.

Effects of growth diagnosis based on canopy image taken by smartphone and variable-rate nitrogen fertilization on the yield and grain quality of durum wheat cultivar “Setodure”

Effects of growth diagnosis based on canopy image taken by smartphone and variable-rate nitrogen fertilization on the yield and grain quality of durum wheat cultivar “Setodure”

Evaluation of variable rate nitrogen fertilization scenarios in cereal crops from economic, environmental and technical perspective

Optimizing variable rate nitrogen fertilization (VRNF) is required to minimize inputs, maximize outputs, and reduce environmental footprints. However, this is not possible in commercial farms, since it would require to intervene in regular agricultural practices with potential economic and environmental consequences and would take years of costly experiments. This study aims at the evaluation of different VRNF scenarios based on simulations that include the crop nitrogen need, soil variability, nitrogen limits set by legislations and the technical specification of variable-rate machinery. A simulation software was designed and developed to allow automatic prediction and mapping of soil and crop properties, and calculate recommendations for VRNF. The software was used to compare traditional uniform rates nitrogen (N) fertilization (URNF) against three VRNF treatments [e.g., map-based (MB), sensor-based (SB) and map-sensor-based (MSB)] in four commercial fields. Under each of the three treatments two different application schemes were evaluated, e.g., applying more N fertilizer to the more fertile zones (Kings scheme - KS) and more N fertilizer to the least fertile zone (Robin Hood scheme - RHS). Simulations were made after imposing N legislation limits and without imposing them. Finally, VRNF application was evaluated for a full boom sprayer, a section control sprayer and a nozzle control sprayer. Results indicated that the VRNF did not exceed the traditional URNF treatment, but only if the N limit by legislation is imposed. The RHS consumed 16.4–118.1 % less N fertilizer than the KS and 33.3–56.2 % than URNF treatment. However, the KS without N limit always exceeded the applied N fertilizer compared to the URNF treatment, generating high risk of N leaching. When imposing the N limit, both KS and RHS consumed less N than the traditional URNF treatment, except for the MSB under the KS, which used the same N as the URNF treatment. Spatial variability can be observed in the MB treatment, temporal variability in the SB treatment and both variabilities in the MSB treatment. Regarding the spatial variability, no significant differences between section and nozzle control options could be observed, whereas variability was minimized when using the full boom control. We concluded that the optimal fertilization scheme is VRNF based on a combination of RHS and section control, which is expected to result in reducing N fertilizer application cost, minimising risk of N leaching, and ensuring N applied is under the set legislation limits. Future work should include the crop response in the analysis to provide a comprehensive evaluation of the optimal VRNF treatment.

Comparisons of Uniform and Variable Rate Nitrogen Fertilizer Applications in Real Conditions - Evaluation of Potential Impact on the Yield of Wheat Available for Use in Animal Feed

The presented research deals with the variable application of N fertilizers (VRA NF) in the stand of winter wheat and with possibilities for evaluating the effect of such application on grain yield. The effect of VRA NF was assessed in close cooperation between Spearhead Czech Ltd. and Mendel University in Brno within an operational experiment. In 2018 and 2019, three experimental plots were always chosen on which VRA NF was implemented by conventional technique according to application maps. Each application map included control strips with the uniform NF application. The application maps were prepared based on the spectral analysis of satellite images. The individual plots were divided into three zones: Zone 1 with the lowest yield potential, Zone 2 with the medium yield potential, Zone 3 with the highest yield potential. The highest dose of N was at all times applied in Zone 3, and conversely the lowest dose was applied in Zone 1. In 2018 and 2019, the experimental fields were harvested by harvester New Holland CX 8080 which was equipped with the technology for the monitoring of grain yields. The main goal of data processing was to remove error data at first and then to re-calibrate them using the information about the weight of harvested grain. The expected benefit, i.e. a yield increase by min. 3% was found only in 2018, when the benefit was about 5%. In the following year, the difference between the conventional application of N and VRA technologies was minimal. However, this condition was probably caused by drought which negatively affected all stands. In particular, measured values of grain yield do not indicate the negative effect of VRA NF on grain yield. This research further showed the applicability of yield data acquired by harvest technology; however, the elimination of error data is necessary as well as their re-calibration according to total yield ascertained by weighing the total production of grain from a specific plot.

Soil nitrous oxide emissions from no-till canola production under variable rate nitrogen fertilizer management

Precision farming methods such as variable rate or site-specific nitrogen (N) fertilizer application based on field management zones may represent a mechanism to mitigate soil nitrous oxide (N2O) emissions. To investigate the impact of site-specific N fertilization on soil N2O fluxes a study was conducted from spring 2013 through summer 2015 on no-till cropland under canola (Brassica napus) production near Brandon, Manitoba, Canada. The static chamber method was used to measure soil N2O fluxes across three management zones (low yield, average yield, and high yield) with three N application rates (50%, 100%, and 150% of target seed yields for each management zone) and unfertilized control treatments. A machine-learning approach (random forest analysis) for analyzing soil N2O fluxes confirmed conditions related to soil temperature, moisture, and nitrate availability were the most important predictors of individual flux events. Similarly, soil N availability and moisture content were found to be the most important variables in regression models of cumulative growing season emissions. Nitrogen fertilizer-induced N2O emission factors were lower for the high yield management zone (≈0.1% both growing seasons) compared to the low yield management zone (0.9% in 2013 and 0.2% in 2015) confirming that variable rate application can mitigate N2O fluxes from cropland soils. The high yield zones had the lowest emission factors despite receiving 50% more fertilizer than field average target yields suggesting more efficient crop N use in these areas with greater production potential. Conversely, the low yield zones had higher emission factors despite receiving 50% less fertilizer than field average target yields indicating a greater environmental burden associated with annual cropping these portions of the agricultural landscape. More studies are required to investigate the generality of these findings for different crops, soils and climatic conditions.

The 13C Discrimination of Crops Identifies Soil Spatial Variability Related to Water Shortage Vulnerability

Spatial variability of crop growth and yields is the result of many interacting factors. The contribution of the factors to variable yields is often difficult to separate. This work studied the relationships between the 13C discrimination (Δ13C) of plants and the spatial variability of field soil conditions related to impacts of water shortage on crop yield. The 13C discrimination, the indicator of water shortage in plants, 15N (δ15N) discrimination, and nitrogen (N) content were determined in grains of winter wheat, spring barley, and pea. The traits were observed at several dozens of grid spots in seven fields situated in two regions with different soil and climate conditions between the years 2017 and 2019. The principles of precision agriculture were implemented in some of the studied fields and years by variable rate nitrogen fertilization. The Δ13C significantly correlated with grain yields (correlation coefficient from 0.66 to 0.94), with the exception of data from the wetter year 2019 at the site with higher soil water capacity. The effect of drought was demonstrated by statistically significant relationships between Δ13C in dry years and soil water capacity (r from 0.46 to 0.97). The significant correlations between Δ13C and N content of seeds and soil water capacity agreed with the expected impact of water shortage on plants. The 13C discrimination of crop seeds was confirmed as a reliable indicator of soil spatial variability related to water shortage. Stronger relationships were found in variably fertilized areas.

Data fusion approach for map-based variable-rate nitrogen fertilization in barley and wheat

Nitrogen (N) is a main parameter affecting crop growth and yield. However, when variable rate N fertilization (VRNF) is adopted, a main question arises of whether to add more on the highly fertile zone or on the less fertile zone. This study aimed to compare two map-based VRNF treatments, e.g., variable-rate 1 (VRNF1) with the application of recommended rate (RR) of N fertilizer plus 40-50% to the high fertility zones, RR to the medium-fertility zones and RR minus 40-50% to the least fertile zones, and VRNF2 following the opposite scenario to that of VRNF1. Recommendations for both VRNF treatments were derived from the fusion of soil data and crop data, e.g., normalized difference vegetation index (NDVI) and historical yield and were compared with traditional uniform rate (UR) application. Online soil measurement was carried out in two fields in Belgium, using a multi-sensor platform that collects high-resolution data on key soil properties using a fiber type visible and near-infrared (vis-NIR) spectrophotometer. The vis-NIR sensor was calibrated using a partial least squares regression (PLSR) to predict pH, organic carbon (OC), moisture content (MC), calcium (Ca), sodium (Na), magnesium (Mg), phosphorous (P) and potassium (K). The predicted soil properties were fused with NDVI and historical yield data to delineate management zone (MZ) maps with different fertility classes. A cost-benefit analysis was carried out using the cost of fertilizer applied as input cost and the price of barley and wheat in 2019. Results showed that the VRNF2 treatment was more profitable compared to both VRNF1 (11.55 and 19.52 EUR ha-1) and UR (16.40 and 148.78 EUR ha-1). The VRNF2 treatment maintained or increased crop yield by up to 10.4% and reduced the amount of N fertilizer applied by up to 19.4%, compared to the traditional UR. Because of the lower amount of N fertilizer used compared to the other two treatments, the VRNF2 would reduce the negative environmental impact by reducing the amount of N fertilizer applied. Therefore, we recommend the VRNF2 based-on data fusion of soil and crop parameters as the best approach to manage N fertilization site specifically.

Çim bitkisinin azot ve su içeriği tahmini için Renk ölçer ve NDVI ölçerin karşılaştırılması

Aims: Nitrogen content (NC) and water content (WC) of turfgrass is traditionally determined by laboratory analysis which is time-consuming, tiresome, laborious and costly. The aim of this study was to examine the suitability of two hand held optical instruments (GreenSeeker NDVI meter and chromameter) to evaluate NC and WC of turfgrass.Methods and Results: Six turfgrass plots of 1 m x 1 m with a mixture of five different species were used and variable rate nitrogen fertilizer (N0: 0 g N m-2, N1: 2.5 g N m-2, N2: 5 g N m-2) was applied. NDVI measurements were taken at around noon with a GreenSeeker NDVI instrument from the plots. After mowing, the color values of the clippings were measured using a hand-held chromameter. The data were analyzed using correlation and partial least square regression (PLSR). A high correlation was found between leaf NC, WC, NDVI and color values. The leaf NC (%) can be estimated from the NDVI (R2val=0.73, SEP=0.19%) and color values (L*a*b*C*h°) (R2val=0.76; SEP=0.18%). Also, it was found that the WC (%) can be predicted from the NDVI (R2val=0.40, SEP=5.07%) and color values (L*C*h°) (R2val=0.69; SEP=3.67%) with slightly lower accuracy.Conclusions: Turfgrass leaf NC can be estimated with either an NDVI instrument (R2=0.73, SEP=0.19%) or a chromameter (R2=0.76, 0.18%) with reasonable accuracy in a more objective and economical way.Significance and Impact of the Study: Considering the reduction in time and cost required in the NC and WC analysis, we think that results of this study may be useful for turf field managers. Also, nitrogen determination with sensors will be a more eco-friendly way if used by managers.

May smart technologies reduce the environmental impact of nitrogen fertilization? A case study for paddy rice.

Precision agriculture is increasingly considered as a powerful solution to mitigate the environmental impact of farming systems. This is because of its ability to use multi-source information in decision support systems to increase the efficiency of farm management. Among the agronomic practices for which precision agriculture concepts were applied in research and operational contexts, variable rate (VR) nitrogen fertilization plays a key role. A promising approach to make quantitative, spatially distributed diagnoses to support VR N fertilization is based on the combined use of remote sensing information and few smart scouting-driven ground estimates to derive maps of nitrogen nutrition index (NNI). In this study, a new smart app for field NNI estimates (PocketNNI) was developed, which can be integrated with remote sensing data. The environmental impact of using PocketNNI and Sentinel 2 products to drive fertilization was evaluated using the Life Cycle Assessment approach and a case study on rice in northern Italy. In particular, the environmental performances of rice fertilized according to VR information derived from the integration of PocketNNI and satellite data was compared with a treatment based on uniform N application. Primary data regarding the cultivation practices and the achieved yields were collected during field tests. Results showed that VR fertilization allowed reducing the environmental impact by 11.0% to 13.6% as compared to uniform N application. For Climate Change, the impact is reduced from 937.3 to 832.7kg CO2 eq/t of paddy rice. The highest environmental benefits - mainly due to an improved ratio between grain yield and N fertilizers - were achieved in terms of energy consumption for fertilizer production and of emission of N compounds. Although further validation is needed, these preliminary results are promising and provide a first quantitative indication of the environmental benefits that can be achieved when digital technologies are used to support N fertilization.

Challenges of multi/hyper spectral images in precision agriculture applications

According to the CEMA, Agriculture 4.0 paves the way for the next evolution of farming consisting of unmanned operations and autonomous decision systems while Agriculture 5.0 will be based around robotics and (some form of) artificial intelligence. It is clear how Agriculture is experimenting a Copernican Revolution where multidisciplinarity is the engine of this revolution. In this context it is central the topic of Variable Rate Treatments (VRTs). A VRT application relies on prescription maps that are generated by considering the agronomist experience augmented with data sensed also by using advanced platform as Unmanned Aerial Vehicles (UAVs) or satellites. Prescription maps are usually implemented by tractors using Variable Rate Controllers (VRCs) that apply a given quantity of product on a given region. The typical operation is spraying nitrogen or weed treatment application over the agricultural field. The generation of a correct prescription map requires the definition of specific management zones that reflect areas and their status. The planning of agricultural tasks necessitates a deep knowledge of crop state, for example, an important but typical case is variable rate nitrogen fertilizer application. In this scenario multi-spectral images play a key role and today the technology is mature to be used also in real applications. Hyper-spectral imagery is still expensive and it is usually adopted for advanced research considering the complexity to acquire and post-process data. However multi and hyper spectral systems are changing the agriculture enabling complex analysis with ultra high spatial and spectral resolution.

Variable-rate nitrogen fertilization of winter wheat under high spatial resolution

Variable-rate application (VRA) addresses in-field variation in soil nitrogen (N) availability and crop response, and as such is a tool for more effective site-specific management. This study assessed the performance of a VRA system for on-the-go delivery of granular fertilizer in 7-m wide and 200-m long strips of a 2.4-ha wheat field. A randomized complete block design consisted of three treatment strips (a preplant uniform application of 100 kg N/ha, a preplant + in-season uniform farmer rate of 212 kg N/ha and a preplant + in-season VRA) within four blocks. The VRA prototype consisted of Crop Circle ACS-430 active canopy sensors, a GeoScout X data logger that processed the geospatial data to convey a real-time N rate signal (1 Hz) to a Gandy Orbit Air 66FSC spreader through a Raven SCS 660 controller. Crop monitoring included analysis of in-season soil and plant samples, water balance and grain yield. VRA delivered an economic optimum N rate using 72% less in-season N or 38% less total N (131 kg N/ha) than that applied by the farmer (212 kg N/ha). The reduction of total N inputs came about without any yield losses and translated to 58% N-use efficiency in comparison to 44% of the farmer practice and 52% of the preplant control. VRA also provided a much higher revenue over fertilizer costs, €68/ha and €118/ha higher than the preplant control and the farmer practice, respectively. The return of VRA per unit of N was equal to that of the large preplant application due to low leaching losses. Overall, the high-resolution VRA was superior in terms of environmental benefits and profitability with the least uncertainty to the farmer.

Impacts of Variable Rate Nitrogen (VRN) on Nitrate-N Losses from Tile Drained Maize in Minnesota, USA

Little is known about the effectiveness of sensor-based variable rate nitrogen (VRN) fertilizer application at reducing nitrate-N losses. A variable rate in-season nitrogen (VRN) treatment was compared to a conventional pre-plant uniform rate nitrogen treatment in eight Minnesota, USA sub-fields. Water samples collected weekly during 2016 were analyzed for nitrate-N. The Drainmod model was able to accurately represent measured tile discharge and nitrate-N concentration data. Modeled nitrate-N losses in tile discharge were 16% lower in VRN than uniform treatments. These results show that in-season VRN fertilizer N management can significantly reduce nitrate-N loads from tile drained maize fields.

A tool based on remotely sensed LAI, yield maps and a crop model to recommend variable rate nitrogen fertilization for wheat

Inversing the STICS crop model with remote-sensing-derived leaf area index (LAI) and yield data from the previous crop is used to retrieve some soil permanent properties and crop emergence parameters. Spatialized nitrogen (N) fertilization recommendations are provided to farmers, for the second and third N applications, following the screening of eleven N application rates under a range of possible forthcoming climates, with the objective to maximize of the gross margin while respecting some environmental constraints. As a first field validation, we show (1) the improvement brought by the assimilation of LAI and yield into STICS to simulate crop and soil variables and (2) the interest of site specific application to maximize both the gross margin and the agro-environmental criterion.

Early stage variable rate nitrogen fertilization of silage maize driven by multi-temporal clustering of archive satellite data

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50kgNha−1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350kgNha−1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.

Variable rate nitrogen fertilizer response in wheat using remote sensing

Nitrogen (N) fertilizer application can lead to increased crop yields but its use efficiency remains generally low which can cause environmental problems related to nitrate leaching as well as nitrous oxide emissions to the atmosphere. The objectives of this study were to: (i) to demonstrate that properly identified variable rates of N fertilizer lead to higher use efficiency and (ii) to evaluate the capability of high spectral resolution satellite to detect within-field crop N response using vegetation indices. This study evaluated three N fertilizer rates (30, 70, and 90 kg N ha−1) and their response on durum wheat yield across the field. Fertilizer rates were identified through the adoption of the SALUS crop model, in addition to a spatial and temporal analysis of observed wheat grain yield maps. Hand-held and high spectral resolution satellite remote sensing data were collected before and after a spring side dress fertilizer application with FieldSpec, HandHeld Pro® and RapidEye™, respectively. Twenty-four vegetation indices were compared to evaluate yield performance. Stable zones within the field were defined by analyzing the spatial stability of crop yield of the previous 5 years (Basso et al. in Eur J Agron 51: 5, 2013). The canopy chlorophyll content index (CCCI) discriminated crop N response with an overall accuracy of 71 %, which allowed assessment of the efficiency of the second N application in a spatial context across each management zone. The CCCI derived from remotely sensed images acquired before and after N fertilization proved useful in understanding the spatial response of crops to N fertilization. Spectral data collected with a handheld radiometer on 100 grid points were used to validate spectral data from remote sensing images in the same locations and to verify the efficacy of the correction algorithms of the raw data. This procedure was presented to demonstrate the accuracy of the satellite data when compared to the handheld data. Variable rate N increased nitrogen use efficiency with differences that can have significant implication to the N2O emissions, nitrate leaching, and farmer’s profit.

Evaluation of Sensor-based Variable Rate Nitrogen Fertilization for Winter Wheat Production

Evaluation of Sensor-based Variable Rate Nitrogen Fertilization for Winter Wheat Production