Variable Fertilizer Application Research Articles

Precise Placement and Variable Rate Fertilizer Application Technologies for Horticultural Crops

Fertilizer spreaders capable of variable rate application are increasingly important for enhancing nutrient management in horticultural crops because they improve placement and increase nutrient uptake efficiency. Matching applied fertilizer to fertilizer requirements represents a significant input cost saving for the grower and a reduction in potential pollutant loading to ground and surface water. Variable rate fertilization (VRF) is a precision agriculture technology made possible by embedded high-speed computers, accurate Global Positioning System (GPS) receivers, Geographic Information Systems (GIS), remote sensing, yield or soil maps, actuators, and electronic sensors capable of measuring and even forecasting crop properties in real time. For tree crops like Florida citrus (Citrus spp.), the most important function of the VRF spreader is to detect and avoid fertilizing spaces of the orchard not occupied by trees. Treeless spaces are becoming more common in Florida as diseases such as citrus greening (Candidatus Liberibacter asiaticus) and canker (Xanthomonas axonopodis) cause the removal of thousands of trees every year. VRF works best under those conditions. Because VRF exploits crop and soil variability, it has no value in a perfectly uniform field. VRF enables smaller trees including resets to be fertilized at lower, most appropriate rates, thus minimizing any excess application. This article examines the existing knowledge on using precision agriculture and variable rate technology to keep water and nutrients in the root zone of horticultural crops, thus facilitating maximum uptake efficiency.

Grid soil sampling adoption and abandonment in cotton production

Technology adoption in precision agriculture has received considerable attention, while abandonment has received little. Survey data are now available to evaluate adoption and abandonment decisions. Understanding the factors motivating technology adoption and abandonment has implications for educational efforts directed toward improving the efficiency of production inputs and for research and development to improve the value of precision agriculture technologies. The objective of this research was to identify factors motivating the adoption and abandonment of grid soil sampling in precision cotton production. These decisions were evaluated assuming a random utility model. Data were obtained from a 2005 survey of cotton producers in 11 Southeastern states in the USA. Results from limited dependent variable regressions indicate that younger producers who farmed more cotton area, owned more of their cropland, planted larger amounts of non-cotton area, used a computer for farm management and used a Personal Digital Assistant (PDA) in the field were more likely to adopt grid soil sampling for cotton precision farming. Results also suggest that producers with more cotton area who owned livestock and adopted management zone soil sampling were more likely to abandon grid soil sampling, while those who used a PDA in the field, used grid soil sampling for more years and followed up grid soil sampling with variable-rate fertilizer application were less likely to abandon grid soil sampling for cotton production.

Prediction Soil Fertilization Maps Using Logistic Modeling and a Geographical Information System

Multivariable fertilizer recommendation (MVFR) models allowed for variable rate fertilizer applications and precision agriculture. The objectives of this study were (i) to use binary logistic modeling to assess the importance of site variables in farms' decisions for implementing optimal N and K fertilization, and (ii) to combine multivariable logistic models in a geographic information system for the prediction of N and K fertilization class maps. These were based on experimental optimal N and K fertilization data at 67 sites and survey data for 22 site variables in an arid date palm (Phoenix dactylifera L.) region (20,000 ha). Only nine of the 22 site variables were found to be statistically significant in influencing the probability of N and K responses: surface Fe, Mn, and Cu; profile residual NO3–N; surface organic matter (OM), sand, and clay; surface extract soil salinity (ECes), and quantity of irrigation water (Qiw). The probability of response (Y = 1 means success and Y = 0 indicates failure) to the levels of minor, major, and excessive N application were expressed by the logistic models: logit[Pr(Y = 1|N = 0.25,0.5,1|X)] = 0.574 − 0.015Qiw + 0.768ECes + 0.455Fe − 1.336 Cu, logit[Pr(Y = 1|N = 0.5,1|X)] = 3.274 − 0.011Qiw − 0.307Mn, and logit[Pr(Y = 1|N = 1|X)] = 4.394 + 1.463ECes − 0.826NO3–N, respectively. The corresponding models for K were: logit[Pr(Y = 1|K = 0.25,0.5,1|X)] = 4.424 − 0.104OM − 0.108NO3–N, logit[Pr(Y = 1|K = 0.5,1|X)] = −1.189 + 0.015Qiw, and logit[Pr(Y = 1|K = 1|X)] = 37.582 − 0.560Mn − 0.036Sand − 0.046Clay. These logistic models were cross‐validated and combined in a geographic information system to derive N and K fertilization class maps using kriged‐interpolated data sets of the significant site variables. Logistic modeling could utilize low‐cost data for MVFR model calibration and validation and the production of soil fertilization maps with larger scales.

Spatial Variability of Soil Chemical Properties in the Reclaiming Marine Foreland to Yellow Sea of China

Precise information about the spatial variability of soil properties is essential in developing site-specific soil management, such as variable rate application of fertilizers. In this study the sampling grid of 100 m × 100 m was established to collect 1 703 soil samples at the depth of 0-20 cm, and examine spatial patterns including 13 soil chemical properties (pH, OM, NH 4 +, P, K, Ca, Mg, S, B, Cu, Fe, Mn, and Zn) in a 1 760 ha rice field in Haifeng farm, China, from 6th to 22nd of April, 2006, before fertilizer application and planting. Soil analysis was performed by ASI (Agro Services International) and data were analyzed both statistically and geostatistically. Results showed that the contents of soil OM, NH 4 +, and Zn in Haifeng farm were very low for rice production and those of others were enough to meet the need for rice cultivation. The spatial distribution model and spatial dependence level for 13 soil chemical properties varied in the field. Soil Mg and B showed strong spatial variability on both descriptive statistics and geostatistics, and other properties showed moderate spatial variability. The maximum ranges for K, Ca, Mg, S, Cu and Mn were all ∼ 3 990.6 m and the minimum ranges for soil pH, OM, NH 4 +, P, Fe, and Zn ranged from 516.7 to 1 166.2 m. Clear patchy distribution of N, P, K, Mg, S, B, Mn, and Zn were found from their spatial distribution maps. This proved that sampling strategy for estimating variability should be adapted to the different soil chemical properties and field management. Therefore, the spatial variability of soil chemical properties with strong spatial dependence could be readily managed and a site-specific fertilization scheme for precision farming could be easily developed.

Site-specific management of limiting nutrients in peanut (Arachis hypogaea) on the Texas High Plains

Nutrient data obtained from soil chemical tests were analyzed in an activity analysis model to identify limiting factors in peanut production on the Texas High Plains. A production function was estimated for the study field, and limiting factors were identified at individual sites where the production function indicated that yield did not respond. The estimated production function also enabled us to conduct a cost-return analysis of variable- and blanket-rate fertilizer applications. The results showed that peanut yields did not respond to most of the nutrients included in the study, which confirmed conclusions from previous studies in the study region. Calcium and nitrogen were the only two limiting factors identified in this study. Significant economic returns could be obtained by site-specific fertilizer application. The average economic return from variable-rate calcium fertilizer application was $27.84 ha−1 and from blanket-rate it was $10.73 ha−1. The return from variable-rate nitrogen fertilizer application was about $20 ha−1 and from a blanket-rate it was about $14 ha−1. There seems to be quite a strong economic incentive to adopt variable-rate application for calcium and nitrogen fertilizer application.

A Variable Rate Fertilizer Applicator Mounted on a Paddy Vehicle

A Variable Rate Fertilizer Applicator Mounted on a Paddy Vehicle

Evaluation of a Low-Cost GPS Receiver for Precision Agriculture Use in Adana Province of Turkey

The most vital factor in the application of precision agriculture technology is the cost of the required high-technology equipment. The equipment cost is the major obstacle in adopting the precision agriculture. GPS receiver is one of the most essential tools with high initial costs in this technology. The aim of this study was to evaluate a low-cost GPS receiver in 3 different tests including static, dynamic circular area, and dynamic straight line tests. It was observed that the tested low-cost GPS receiver yielded a deviation of less than 1.50 m, 1.60 m, and 1.48 m in static, circular area, and straight line tests, respectively. It can be concluded that the low-cost GPS receiver without differential correction can be used for variable fertilizer application and soil and yield mapping since it has an appropriate accuracy values for these applications. On the other hand, it would not be suitable for some precision agriculture applications that require an accuracy of less than 1 m such as variable herbicide application and row crop planting. Instead, a GPS receiver with differential correction service should be employed for such applications. In addition, the mean percent error values were -1.3% and -0.5% in all tests in the circle area calculation. These values can be considered to be acceptable for the field area calculation studies.

Adoption and Abandonment of Precision Soil Sampling in Cotton Production

Adoption of precision agriculture technology has received considerable attention, but abandonment has received little. This paper identifies factors motivating adoption and abandonment of precision soil sampling in cotton. Younger producers who farmed more cotton area, owned more of their cropland, planted more non-cotton area, or used a computer were more likely to adopt precision soil sampling. Those with more cotton area or who owned livestock were more likely to abandon, while those who used precision soil sampling longer, or used variable-rate fertilizer application were less likely to abandon precision soil sampling.

Economics of site-specific nitrogen management for protein content in wheat

Wheat fields in the semiarid region of Argentina are spatially variable in soil nitrogen (N) fertility and crop productivity. By accounting for spatial variation in soil N levels, variable-rate fertilizer application may improve crop yield, protein content, and N use efficiency within fields. Therefore, there is interest in applying variable rates of N fertilizer across the landscape. The general objectives of this research are to determine relationships among yield, protein and N rates, using spatial regression analysis of yield monitor data; and to optimize variable-rate application (VRA) of nitrogen fertilizer for wheat. The data were drawn from an on-farm N trial of 10.2 ha within a 44 ha field conducted in Manfredi, Córdoba, Argentina, in 2003. The experimental design was a complete block strip trial that included two different types of soils in terms of landscape (Hilltop and Lowland) and two different antecessor crops (Corn and Soybeans). The fertilized strips were wider than the combine platform width, with zero N application as the control, and five other rates of elemental N (12, 37, 62, 88 and 112 kg ha −1). Yield data were obtained with a combine grain monitor, and grain samples for quality analysis were manually collected from the grain flow of a combine harvester, analyzed for quality in the laboratory and converted into a geographical information system (GIS) layer, together with the yield monitor data. Yield and grain quality variability was observed across the field, and among treatments. The combination of yield maps, soil moisture and protein content can help to determine management zones in order to maximize economic benefit. This approach offers opportunities to optimize grain protein on a site-specific basis by accounting for spatial variability of N fertility within individual fields.

입제 비료 변량 살포 제어시스템의 분석 및 설계

This study was conducted to improve the control performance of a current variable-rate controller for granular fertilizers. Simulation model was developed. Optimized proportional, integral and derivative gains were determined by simulation model using 2nd order PID gain learning algorithm, and these control gains were evaluated through the field tests. Important results of this study are as follows; 1. Principles of pre-existing variable-rate application of granular fertilizers were investigated. 2. Simulation model of a PID controller that could simulate the control system was developed by using Matlab/Simulink program. The program was to determine PID control coefficients through the simulation model and 2nd order PID gain learning algorithm. 3. PID control coefficients obtained from the simulation were applied to the developed model. When the step input was given, Maximum overshoot were 1.96%, rise time were 0.05 sec, settling time were 0.06 sec and steady state error were 0.21 % respectively. 4. The simulation model was verified through field tests. The errors of maximum overshoot were 10%, rise time were 0.11 sec, settling time were 0.40 sec and steady state error were 8% because of loads and noises. Rise time was decreased to one third of that of the pre-existing system. 5. If the speed of a fertilizing machine is <TEX>$0.3{\sim}0.6\;m/s$</TEX> and the maximum rotation speed of a discharging roller is 64 rpm, rise time would be 0.26 sec and fertilizing machine would cover the distance of <TEX>$0.07{\sim}0.15\;m$</TEX> with settling time of 0.4 sec, fertilizing machine would cover the distance of <TEX>$0.12{\sim}0.24\;m$</TEX>.

입제 비료의 변량 살포를 위한 제어기 개발

This study was conducted to design and fabricate a controller for variable-rate application of granular fertilizer based on physical and chemical information, to analyze the performance of the controller and characteristics of a discharger. The result of the study are summarized as follows: 1. The charge ratios of discharger by accumulation heights of fertilizer in hopper were examined, and the variations in charge ratio were <TEX>$72.58{\sim}93.33%$</TEX> and <TEX>$63.14{\sim}94.42%$</TEX> for the fertilizers Super 21 and Sinsedae, respectively. The charge ratio also decreased as the rotational speed of discharger increased. 2. The coefficient of variation of discharge amount by rotational speed and discharge time of discharger were in the range of <TEX>$2.94{\sim}11.23%$</TEX> and <TEX>$2.82{\sim}10.80%$</TEX> for the fertilizer of Super 21 and Sinsedae. Except the rotational speed of 12 rpm, the coefficient of variation for discharge amount were relatively small with 4% more or less 3. In order to evaluate the rotational speed of discharger, the control signal in the range of <TEX>$0{\sim}5V$</TEX> was subdivided into the 50 steps by 0.1V. The regression equation for the rotational speed of discharger was Y=55.984X-79.174(X: input voltage, V, Y: discharger speed, RPM) and the <TEX>$R^2$</TEX> was 0.99. 4. In order to evaluate the performance of the controller for variable-rate application of granular fertilizer, settling time to unit step input was examined. The settling time varied from 0.8sec to 1.4 sec.

Evaluating Soil Color with Farmer Input and Apparent Soil Electrical Conductivity for Management Zone Delineation

Variable rate fertilizer application technology (VRT) can provide an opportunity to more efficiently utilize fertilizer inputs; however, accurate prescription maps are essential. Researchers and farmers have understood the value of dividing whole fields into smaller, homogeneous regions or management zones for fertility management. Management zones can be defined as spatially homogeneous subregions within a field that have similar crop input needs. Delineating management zones that characterize the spatial variability within a field may provide effective prescription maps for VRT. The objective of this research was to compare and evaluate management zones developed from soil color (SC) and farmer experience with management zones developed from apparent electrical conductivity (ECa). These two methods of developing management zones were compared with soil nutrient levels, texture, and crop yields collected on two fields in 1997. The soil and yield parameters followed the trends indicated by both management zone methods at Field 1 with the highest values found in the high productivity zones and the lowest the low productivity zones. Significant differences were found among the management zones. However, at Field 2 the high and medium productivity zones were generally not significantly different using the SC approach, whereas the ECa approach was effective in identifying three distinct management zones. Both methods of developing management zones seem to be identifying homogeneous subregions within fields.

Runoff Water Quality Impact of Variable Rate Sidedress Nitrogen Application

In recent years, precision agriculture has received attention from producers, agribusiness, and governmental agencies in an effort to increase profitability and protect the environment. Many aspects of precision agriculture, such as soil fertility, application technology, and economic factors, have received substantial research attention; however, other aspects of precision agriculture have not been well documented. One important issue that warrants increased attention is water quality. Because of precision application technology, variable rate fertilizer application based on within-field heterogeneity has the potential to decrease negative water quality impacts. Therefore, the objective of this paired watershed study was to evaluate the impact of variable rate nitrogen (N) fertilizer application on surface water quality. The variable rate field was divided into management units designated as poor, moderate, and high based on measured yield potential and received 100–160 kg/ha of N fertilizer. A portion of the N application was uniformly applied pre-plant or at planting, and rest was sidedressed at variable rates. The uniform rate field received uniform N application at 135 kg/ha. Surface water runoff and water quality were monitored for each field, and collected samples were analyzed for N and phosphorus (P) constituents. During the 2-year monitoring period with 22 storm sampling events, variable rate N application resulted in few water quality differences compared to uniform rate application, but overall median NO3 + NO2–N concentrations were significantly lower for the variable rate field in the second year of variable rate N application. Overall and event mean NO3 + NO2–N concentrations from the variable rate field tended to be higher, but median concentrations from the uniform rate field tended to be higher.

Remote sensing applications for precision agriculture: A learning community approach

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing, Geographic Information Systems (GIS), and Global Positioning Systems (GPS) may provide technologies needed for farmers to maximize the economic and environmental benefits of precision farming. However, most farmers do not have the skills to utilize these technologies effectively. Through a learning community approach led by the Upper Midwest Aerospace Consortium (UMAC), information was shared among scientists, agricultural producers, and data providers. Farmers and ranchers received value-added information derived from AVHRR, MODIS, ETM+, IKONOS, DigitInc's DALSA camera system and Positive Systems' ADAR 5500 digital aerial camera, over four growing seasons. Emphasis has been placed on reducing the time between data acquisition and delivery of value-added products to farmers, developing practical uses for the data and providing basic training so that the end users could understand how to interpret the information. Farmers and ranchers in rural areas were connected via wide-bandwidth satellite link to a central distribution center at the University of North Dakota. The farmers participated actively in evaluating the usefulness of inputs derived from remotely sensed data, sometimes even by conducting experiments on fertilizer and fungicide applications and assessing the economic benefits. Resulting applications included management zone delineation, verifying the effectiveness of variable-rate fertilizer applications, verifying the effectiveness of fungicide applications, quantifying the loss due to accidental spray drift damage, selecting acres within sugar beet fields under the Payment in Kind program, and monitoring physical damage due to insect, inundation, wind and hail. Several other in-field, early season management practices were also reviewed using high-resolution images.

Long-Term Variable Rate Lime and Phosphorus Application for Piedmont No-Till Field Crops

Variable rate (VR) fertilizer application is a paradigm with potential to improve input efficiency and farm profitability. It is widely marketed by commercial applicators in the southeastern US. However, field studies comparing VR with traditional management have not demonstrated consistent, positive results. The objectives of this study were: (1) to determine the soil impact, crop response and economic potential of VR phosphorus (P) and lime application in a North Carolina Piedmont no-till field crop system using intensive soybean [Glycine max (L.) Merr.] production and (2) to economically evaluate alternatives to standard commercial grid soil sampling for directing VR P and lime. A 23-ha long-term no-till field in the SE Piedmont was divided into 0.4ha plots assigned to either VR or uniform P and lime application. Grid soil sampling and VR P and lime application were done prior to four crops over 3 years: full season soybean, wheat (Triticum aestivum L.)–double cropped soybean, and full season soybean. Soil test P, pH and crop yield response to VR P were inconsistent. Soil pH in areas with low pH initially did increase in response to VR lime, but it took two to three applications to bring all of these areas to the target pH. Once VR-liming raised initially low soil pH to levels close to target, yield of soybean, but not wheat, were up to 0.74Mg ha−1 higher than with uniform lime. Even with significantly higher soybean yields associated with VR lime, 3 years of grid sampling and VR application were not profitable compared to uniform application. The results indicated that VR lime could be profitable if the initial grid sampling data were used either for 2 consecutive years, or if it was used to restrict future grid sampling to specific areas requiring further VR lime.

A Geostatistical Analysis of the Spatial Variation of Soil Mineral Nitrogen and Potentially Available Nitrogen Within an Arable Field

The technology for site-specific applications of nitrogen (N) fertilizer has exposed a gap in our knowledge about the spatial variation of soil mineral N, and that which will become available during the growing season within arable fields. Spring mineral N and potentially available N were measured in an arable field together with gravimetric water content, loss on ignition, crop yield, percentages of sand, silt, and clay, and elevation to describe their spatial variation geostatistically. The areas with a larger clay content had larger values of mineral N, potentially available N, loss on ignition and gravimetric water content, and the converse was true for the areas with more sandy soil. The results suggest that the spatial relations between mineral N and loss on ignition, gravimetric water content, soil texture, elevation and crop yield, and between potentially available N and loss on ignition and silt content could be used to indicate their spatial patterns. Variable-rate nitrogen fertilizer application would be feasible in this field because of the spatial structure and the magnitude of variation of mineral N and potentially available N.

Uncertainty and spatial variability: incentives for variable rate technology adoption in agriculture

This paper develops a behavioral model of farmer decision making to analyze the incentives for adoption of a technology that provides information about spatial variability in nutrient availability and enables variable rate application of fertilizers. It examines the implications of uncertainty about the accuracy of the technology on input application and adoption decisions. Risk aversion and uncertainty can lead to the imposition of a risk premium on input use, which create incentives to over or under apply fertilizers. This influences not only the incentives to adopt the technology but also the impact of adoption on pollution. High spatial variability mitigates the disincentives for adoption even if fixed costs are high and farm size is small but the extent to which this occurs depends on the degree of risk aversion and uncertainty. These results have implications for the design and effectiveness of cost–share subsidies to induce adoption and reduce over application of fertilizers.

Adding value to spatially managed inputs by understanding site-specific yield response

Many mechanized crop producers and agribusinesses are fascinated with precision agriculture technology, but adoption has lagged behind the expectations. Among the reasons for slow adoption of precision agriculture technology is that initial users focused excessively on in-field benefits from variable-rate fertilizer application using regional average fertilizer recommendations. This article illustrates how greater use of site-specific crop response information can improve variable rate input application recommendations. Precision agriculture is spatial information technology applied to agriculture. The technologies include global position systems (GPS), geographic information systems (GIS), yield monitoring sensors, and computer controlled within-field variable rate application (VRA) equipment. Experimentation with these technologies is occurring everywhere there is large scale mechanized agriculture. Commercial use has been greatest in the US, where 43% of farm retailers offered VRA services in 2001. Except for certain high-value crops like sugar beet, farmer adoption of VRA has been modest. The farm level profitability of VRA continues to be questionable for bulk commodity crops. The theoretical model and illustration presented here suggest that VRA fertilization has not yet reached its profitability potential. Most VRA field trials to date have relied upon existing state-wide or regional input rate recommendations. Unobserved soil characteristics can potentially interact with an input to make its effect on yield vary site-specifically within fields. Failure to use site-specific response functions for VRA applications may lead to a misallocation of inputs just as great as that which results from using uniform applications instead of VRA. Agricultural economists have a long history of estimating output response to input applications. Several have started to develop tools to estimate site-specific responses from yield monitor and other precision agriculture data. Likewise, agricultural economists have developed an important body of research results on information value based on managing variability—typically in temporal settings. With these tools, a major potential exists to develop further benefits from precision agriculture technologies that permit truly spatially tailored input applications.

Differentiating Soil Types Using Electromagnetic Conductivity and Crop Yield Maps

Variable rate technology enables management of individual soil types within fields. However, correct classification of soil types for mid‐Atlantic coastal plain soils are currently impractically expensive using an Order I Soil Survey, yet variable rate fertilizer application based on soil type can be highly effective. The objectives of this study were to determine if apparent electromagnetic conductivity (ECa) alone or combined with previous year crop yields using global positioning system technology can provide a useful alternative to detailed soil mapping. The site contained alluvial soils ranging from Bojac 1 and 2 (coarse‐loamy, mixed, thermic, Hapludults) to Wickham 3 and 4 (fine‐loamy, mixed, thermic, Ultic Hapludalfs). The two fields totaled approximately 24 ha. A statistical nonparametric classification method, called recursive binary classification trees, was used to determine how well soil types could be classified. Electromagnetic conductivity readings and crop yields were positively correlated. Broad patterns in the relationship between soil types and ECa readings and crop yields existed for all crop combinations considered. Lower ECa readings and crop yields corresponded to the Bojac soils, while higher ECa readings and crop yields were categorized as Wickham soils. Electromagnetic induction alone correctly classified the soils into broad categories of Bojac or Wickham with over 85% accuracy. When ECa was combined with crop yield data, correct classification rose to over 90%. More precise classification into Bojac 1, Bojac 2, and Wickham soils yielded slightly lower correct classifications ranging from 62.6 to 81.2% for ECa alone, and 80.3 to 91.5% when combined with various crop yields.

Soil Phosphorus and Potassium Mapping Using a Spatial Correlation Model Incorporating Terrain Slope Gradient

Variable-rate fertilizer application requires knowledge of the spatial distribution of soil nutrients within fields. Grid soil sampling might be used for acquiring this information, but is often too expensive for resolving spatial patterns in soil nutrients at the scale of precision fertilizer application. The objective of this study was to determine whether grid sampling efficiency can be improved using cokriging estimates with slope gradient as a secondary variable, which is easily obtained from high-resolution digital elevation models. Soils in two northern Montana wheat fields were sampled at the nodes of a 100-m diagonal grid. Soil test phosphorus and potassium maps were constructed with kriging and cokriging. Co-kriging uses the spatial correlation between two variables to predict for the less intensively sampled variable of interest, often with less estimation error than a univariate method such as kriging. The average estimation variance for cokriging compared to kriging was reduced for all values of the correlation considered. The additional complexity of cokriging might be justified provided a secondary variable exists that is spatially cross correlated with the primary variable of interest.