Probability of profitable yield response to nitrification inhibitor used with liquid swine manure on corn

Nitrification inhibitors (NI) can be used with liquid swine manure (LSM) to decrease potential NO₃ losses, but knowledge specifying when and where NI can increase corn (Zea mays L.) yields is limited. Eleven on-farm evaluation trials (OET) were conducted in 2009 and 15 in 2010 to identify site-specific factors for using Instinct (an encapsulated form of nitrapyrin) with LSM in Iowa. Farmers injected LSM in the fall in at least three field-long strips with and without NI. Yield responses (YR) to NI were calculated by dividing yield monitor data into 50-m cells within each field. Hierarchical models were used to estimate predictive probabilities of profitable YR for two categories of monthly average rainfall and soil drainage. On average, NI produced no YR in relatively normal 2009 and a 0.15 Mg ha^?1 YR in extremely wet 2010. The NI did not change late-season corn N status but half of corn stalk nitrate test (CSNT) samples were N deficient in 2009 and about 65 % in 2010. Fields receiving >90 cm March through August rainfall in 2010 were predicted 65 % more likely to have economic YR (>0.13 Mg ha^?1) than fields receiving <90 cm rainfall. Within-field variability in YR was about four times greater than among-field variability, but within field-level factors had no significant effects on YR. The NI effects may not have lasted long enough to increase yields across all OET and predictive probabilities suggest that NI may produce profitable YR only when spring and summer rainfall exceed the long-term averages by more than 40 %.     

Sensing soil and foliar phosphorus fluorescence in <Emphasis Type="Italic">Zea mays</Emphasis> in response to large phosphorus additions

Additions of large loads of phosphorus (P) enriched animal manure to soils and the persistence of their environmental impact have been associated with continued water quality impairment in regions of high density of confined animal feeding operations. Foliar P in corn (Zea mays L.) and changes in labile P in Aquic Hapludults were determined following P application of 0–560 kg P ha^?1 as KH₂PO₄ and an application of Fe³⁺ (150 mg Fe³⁺ kg^?1) in field mini-lysimeters to develop calibrations of soil and plant nutritional responses. X-ray fluorescence (XRF) scanning of uppermost leaves of plants at the V2, V5, and V8 stages showed that foliar P proportionally increased with addition rates. Exchangeable and enzyme-labile P forms were effective indicators of foliar XRFS-P for up to 30 days after emergence. Phosphorus calibration curves developed for flag leaves showed that spatial distribution of foliar P (3.6, 4.2, and 5.3 g kg^?1) corresponded to field zones treated with 0, 15, and 30 kg P ha^?1 as dairy manure P for the past 18 years. Up-to-date crop uptake and availability of P in these Hapludults were best described by a square root function of soil XRFS-P and total exchangeable inorganic P (r² = 0.4; RMSE = 419 and 422 g ha^?1, respectively). Therefore, a timely knowledge of canopy P status and its linkage to actual soil P status supports in situ element-specific sensing and precision nutrient management in order to manage the declining use-efficiency in crops and reduce potential loss to the environment.     

Uniformity of wheat yield and quality using sensor assisted application of nitrogen

Proximal sensing, or obtaining information from close range, is a potentially useful tool for measuring the crop nitrogen status in real-time The objective of this study was to use proximal sensing of crop canopy spectral reflectance to evaluate variable-rate application of nitrogen in terms of its effect on yield and grain quality of winter wheat (Triticum aestivum L.). The sensor used was the Hydro-Precise N-Sensor System. Yield and grain quality maps were used as a basis for full-scale field trials with winter wheat growing under four nitrogen application treatments: a large (274 kg ha^?1), recommended (167 kg ha^?1) and two sensor-assisted (167 kg ha^?1) rates. The recommended rate of 167 kg N ha^?1 was given in a three-split application that meets the present Danish regulations to reduce nitrogen leaching. These require arable farmers to decrease nitrogen fertilizer application to 90% of the economically optimal level. Each farm’s baseline is calculated to take into account land quality, land allocated to each crop, and crop rotation. In the two sensor-assisted applications the Hydro-Precise N-Sensor System directs the last two of the three-split N application. Grain samples were collected directly from the grain flow of a combine harvester and analysed for protein, water and starch content. Grain data were related to and compared with combine yield meter registrations. Within the field, the variances of protein yield (698–1208 kg ha^?1) and grain protein (9.5–13.4%) were large. The nitrogen application treatments affected the average protein content (10.5–12.3%) and grain yield (9.87–10.42 t ha^?1) strongly. The grain starch content was largest in the uniform and sensor applied systems and smallest in the high nitrogen application treatment. Applying nitrogen according to the Hydro-Precise N-Sensor System did not increase grain yield or the protein and starch contents. Minor differences only were observed in both protein content and yield between uniform-rate N application and sensor-based variable-rate N application.     

Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r² = 0.30 and RMSE = 1.6 Mg ha^?1 in 2012 and r² = 0.37 and RMSE = 4.0 Mg ha^?1, but was strongly correlated for the high biomass sorghum in 2013 (r² = 0.72 and RMSE = 4.6 Mg ha^?1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.     

Simulating field-scale variability and precision management with a 3D hydrologic cropping systems model

Effective variable-rate nitrogen (N) management requires an understanding of temporal variability and field-scale spatial interactions (e.g. lateral redistribution of nutrients). Modeling studies, in conjunction with field data, can improve process understanding of agricultural management. CropSyst-Microbasin (CS-MB) is a fully distributed, 3-dimensional hydrologic cropping systems model that simulates small (10 s of hectares) heterogeneous agricultural watersheds with complex terrain. This study used a highly instrumented 10.9 ha watershed in the Inland Pacific Northwest, USA, to: (1) assess the accuracy of CS-MB simulations of field-scale variability in water transport and crop yield in comparison to observed field data, and (2) quantify differences in simulated yield and farm profitability between variable-rate and uniform fertilizer applications in low, average and high precipitation treatments. During water years 2012 and 2013 (a “water year” refers to October 1st through the following September 30th, where a given water year is named for the calendar year on September 30th), the model simulated surface runoff with a Nash–Sutcliffe efficiency (NSE) of 0.7, periodic soil water content (comparison to seasonal soil core measurements) with a root mean square error (RMSE) ≤0.05 m³ m^?3, and continuous soil water content (comparison to in situ soil sensors) at 15 of 20 microsites with NSE ≥0.4. The model predicted 2013 field variability in winter wheat yield with RMSE of 1100 kg ha^?1. Simulated uniform N management resulted in 0–35 kg ha^?1 greater field average yield in comparison to variable-rate management. The savings in fertilizer costs under variable-rate N management resulted in $23–$32 ha^?1 greater field average returns to risk. This study demonstrated the capacity of CS-MB to further understanding of simulated and observed field-scale spatial variability and simulated crop response to low, medium and high annual precipitation.     

Delineation of specific management areas for coffee cultivation based on the soil–relief relationship and numerical classification

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31.7 ha from which samples were collected at depths of 0.00–0.20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30.4l ha^?1) and best coffee beverage quality (61 sacks ha^?1). Based on the results, we believe that multivariate analysis, geostatistics and the soil–relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production.     

Regional model for nitrogen fertilization of site-specific rainfed corn in haplustolls of the central Pampas,Argentina

In semi-arid regions, soil water and nitrogen (N) are generally limiting factors for corn (Zea mays L.) production; hence, implementation of appropriate N fertilization strategies is needed. The use of precision agriculture practices based on specific site and crop properties may contribute to a better allocation of fertilizer among management zones (MZ). The aim of this study was to develop a model for diagnosis of N availability and recommendation of N fertilizer rates adjusted to MZ for dryland corn crops growing in Haplustolls. The model considered variability between MZ by including site-specific variables [soil available water content at sowing (SAW) and Available Nitrogen (soil available N-NO₃ at planting + applied N, Nd)] using spatial statistical analysis. The study was conducted in Córdoba, Argentina in Haplustolls and consisted in four field trials of N fertilizer (range 0–161 kg N ha⁻¹) in each MZ. The MZ were selected based on elevation maps analysis. Grain yields varied between MZ and increased with larger SAW and Nd at sowing. Grain responses to Nd and SAW in any MZ were not different between sites, allowing to fit a regional model whose parameters (Nd, Nd², SAW, SAW²) contributed significantly (p < 0.001) to yield prediction. Agronomical and economically optimum N rates varied among MZs. However, the spatial variability of optimum N rates among MZs within sites was not enough to recommend variable N fertilizer rates instead of a uniform rate. Variable N fertilizer rates should be recommended only if variability in SAW and soil N among MZ is greater than that found in this work.     

Variable rate spraying application on cotton using an electronic flow controller

Vegetation indices (VI) obtained by optical sensors have a positive correlation with various attributes of cotton plant growth. This work is aimed at evaluating the variable rate application of plant growth regulator (PGR) and fruit ripener on zones defined by VI and penological measurements using a sprayer equipped with a relatively low cost electronic flow controller on the height, percentage of open fruits, yield and net income. The work was done in a 92 ha field during crop seasons 2012/2013 and 2013/2014, and in a 202 ha field, during the crop season 2014/2015. Two spray applications were made using variable rate technology (VRT) of the PGR and one fruit ripener, in both harvest seasons, according to three VI classes formed by a previous mapping. The uniformity of the cotton height and opened fruits contribute to a similar yield across zones. Uniform plant height facilitates cotton harvest. The ripener helps to ensure all the cotton is ready to be harvested at the same time. In this trial, use of VRT technique to manage the PGR and fruit ripener application increased net income by US$152.28 ha^?1, but this estimate is based on yields that are not statistically significantly different from the control. This research confirms that PGR and fruit ripener can be sufficiently managed with an electronic flow controller to result in more uniform cotton plant height and yields within fields, but it leaves open the question of whether VRT PGR is profitable even with the lower cost electronic flow controller.     

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker? active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha^?1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r ² = 0.65). This study shows that the GreenSeeker? active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.     

Underlying causes of yield spatial variability and potential for precision management in rice systems

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha^?1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems.     

Evaluation of agronomic and economic benefits of using RTK-GPS-based auto-steer guidance systems for peanut digging operations

Increasing the peanut (Arachis hypogea L.) digger efficiency by accurate placement over the target rows could minimize damaged pods and yield losses. Producers have traditionally relied solely on tractor operator skills to harvest peanuts. However, as peanut production has shifted to new growing regions in the Southeast US, producers face difficulties digging peanuts under conventional and new management schemes. The present study aimed to: (i) determine the effect of row deviations (RD) of the digger from the target row on peanut yield and quality, and (ii) determine the economic value of using RTK auto-steer guidance systems to avoid tractor deviations during peanut harvest. The study consisted of a randomized complete block design of tillage [conventional (CT) and strip tillage (ST)], row patterns [single (SR) and twin (TWR)] and row deviation (RD_0 mm, RD_90 mm, and RD_180 mm). The RD_90 mm and RD_180 mm treatments exemplify manual driving deviations compared to using an RTK auto-steer guidance system (RD_0 mm). Higher yields and higher net returns resulted from using the RTK auto-steer guidance system. Data showed that for every 20 mm row deviation, an average of 186 kg ha^?1 yield loss can be expected. Overall, yield was higher for the conventional tillage and twin row pattern treatments compared to the other treatments. Yield losses for the SR-CT treatment were higher as the row deviation increased compared with the TWR-CT treatment. In contrast, higher yield losses for TWR-ST compared to SR-ST were observed when deviations of 180 mm occurred instead of digging using the RTK auto-steer guidance system. While a farmer using an RTK auto-steer guidance system with an accuracy within 25 mm (RD_0 mm treatment) could potentially expect additional net returns of between 94 and 404 $ ha^?1 compared to those from row deviations of 90 mm, higher net returns of between 323 and 695 $ ha^?1 could be perceived if the guidance system is used instead of having row deviations of 180 mm. Therefore, the use of RTK auto-steer guidance system will allow growers to capitalize on the increases in yield potential by implementing changes in tillage and row patterns as those evaluated in this study.     

Economic analysis for smart sprayer application in wild blueberry fields

The wild blueberry industry is spending over $80 million CAD per year on agrochemicals for 93 000 ha under production in North America. A pressing need to reduce agro-chemical usage and production cost has resulted in the development of a smart sprayer for spot-application of agrochemicals in wild blueberry fields. This paper encompasses the economic analysis to determine the potential savings for spot-applications of agrochemicals using a smart sprayer. The economic analysis compared the smart sprayer with two other commercially available sprayers (basic and swath control). The swath control sprayer and smart sprayer both featured GPS auto-steer and boom section control to reduce over-spray in already applied areas based on GPS position. The basic sprayer used a foam marker for guidance with no swath control management. The smart sprayer featured a machine vision system that automatically detected target areas in the field further reducing agrochemical input by shutting individual nozzles off in non-target areas in the field. The cost analysis was performed to compare the different features of the sprayer technologies, i.e., base sprayer, additional technology, training, usage, repair and maintenance. The additional components installed on the smart sprayer were justified in terms of agrochemicals/water savings via spot-applications, tractor fuel and operator’s time. The application total cost was $2052 ha^?1 using the basic sprayer, $1799 ha^?1 using the swath control sprayer, and $1138 ha^?1 using the smart sprayer over a 2 year production cycle of the selected fields that were used in this study. The payback period ranged from 2.0 years (60 ha field size) to 9.8 years (20 ha field size) using the swath control sprayer. The payback period ranged from 11 months (60 ha field size) to 3.5 years (20 ha field size) when using the smart sprayer. Results revealed that the smart sprayer had significant advantage from both an environmental and economic perspective over the other two sprayers.     

Assessment of yield monitoring equipment for dry matter and yield of corn silage and alfalfa/grass

Whole farm evaluations have shown that accurate yield data are difficult to collect for alfalfa (Medicago sativa L.) and grass mixtures and corn (Zea mays L.) silage fields. Additionally, on-farm research, a recommended tool for adaptive management, is hindered by lack of practical ways to collect yield data. Recently, forage yield monitors have become available on self-propelled forage harvesters (SPFHs), but precision and accuracy of this technology are unknown. The objective of this project was to evaluate accuracy of yield and moisture sensing components of forage yield monitors for use in alfalfa/grass and corn silage. Moisture content, mass flow weights, total area harvested and total dry yield per hectare were measured on 11 farms in 2013; forage samples were collected for truck loads, analyzed for dry matter content, and compared to monitor-registered dry matter. Truck weights were used to compare monitor-derived yield to actual yield on two farms for alfalfa/grass and three farms for corn silage. Moisture sensors estimated crop moisture content within 3.7 % DM for alfalfa/grass and 3.0 % DM for corn silage of the oven dry value. Flow sensors estimated alfalfa/grass yield to ±0.5 and ±1.1 Mg DM/ha for corn silage. When calibrations are done regularly, forage yield monitors can provide an accurate and precise measure of dry yield for adaptive management. It is concluded that this technology can be used when plots are large and large treatment-driven yield differences are expected.     

Crop height variability detection in a single field by multi-temporal terrestrial laser scanning

Information on crop height, crop growth and biomass distribution is important for crop management and environmental modelling. For the determination of these parameters, terrestrial laser scanning in combination with real-time kinematic GPS (RTK–GPS) measurements was conducted in a multi-temporal approach in two consecutive years within a single field. Therefore, a time-of-flight laser scanner was mounted on a tripod. For georeferencing of the point clouds, all eight to nine positions of the laser scanner and several reflective targets were measured by RTK–GPS. The surveys were carried out three to four times during the growing periods of 2008 (sugar-beet) and 2009 (mainly winter barley). Crop surface models were established for every survey date with a horizontal resolution of 1 m, which can be used to derive maps of plant height and plant growth. The detected crop heights were consistent with observations from panoramic images and manual measurements (R² = 0.53, RMSE = 0.1 m). Topographic and soil parameters were used for statistical analysis of the detected variability of crop height and significant correlations were found. Regression analysis (R² < 0.31) emphasized the uncertainty of basic relations between the selected parameters and crop height variability within one field. Likewise, these patterns compared with the normalized difference vegetation index (NDVI) derived from satellite imagery show only minor significant correlations (r < 0.44).     

An integrated approach to site-specific management zone delineation

Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and cost-effective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%–9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%–38.1%), and soil nutrient and pH variability (9.4%–14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%–48.9%, 16.1%–17.3% and 13.2%–18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.     

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.     

Spectral indices from aerial images and their relationship with properties of a corn crop

Identification of areas with similar restrictions to crop productivity could improve the efficiency to manage agricultural systems, guarantee stable yields, and reduce the effect of droughts in rainfed systems. The ability of any vegetation index to discriminate N and moisture-related changes in leaf reflectance would present an important advantage over the present diagnostic system which involves soil-testing for moisture and available N. The purpose of the study was to calibrate different vegetation indices regarding their capacity to identify water and nitrogen availability for rainfed corn crops in the semiarid Pampas of Argentina. A field experiment with corn with a control without fertilization (N0), and fertilized with 120 kg ha^?1 of nitrogen (N120) was used. Two sites, Low (L) and High (H), were identified within the field, according to their altimetry, a multi-spectral aerial photography was taken from a manned airplane during flowering stage of the corn crop, and four spectral indices were calculated (NDVI, green NDVI, NGRDI, (NIR/GREEN)-1). At six georeferenced points at each site soil texture, organic matter, available phosphorus, nitrogen and moisture contents as well as corn aerial biomass and grain yield were determined. The two sites differed in most of the evaluated soil properties, crop biomass and grain yield. The spectral information obtained at crop flowering showed clear differences between sites H and L for all four indices, indicating that any of these would be able to detect the differences in soil moisture and fertility among these environments. Both (NIR/GREEN)-1 and green NDVI had the best correlation with crop yield determined in the field, and therefore could be considered most appropriate for estimating corn yields from images taken at flowering. For estimation of N requirements, green NDVI differentiated best between fertilized and non-fertilized crop in the moisture limited environment (H), while (NIR/GREEN)-1 performed better in the site where soil moisture was non-limiting (L).     

A synthesis of multi-disciplinary research in precision agriculture: site-specific management zones in the semi-arid western Great Plains of the USA

Researchers from Colorado State University, in collaboration with scientists from the United States Department of Agriculture (USDA), initiated a long-term multi-disciplinary study in precision agriculture in 1997. Site-specific management zones (SSMZ) were investigated as a means of improving nitrogen management in irrigated maize cropping systems. The objective was to develop precise nutrient management strategies for semi-arid irrigated cropping systems. This study was conducted in five fields in northeastern Colorado, USA. Two techniques for delineating management zones were developed and compared: SSMZ and yield-based management zones (YBMZ). Nitrogen uptake and grain yield differences among SSMZs were compared as were soil properties. Both management zone techniques were used to divide fields into smaller units that were different with regard to productivity potential (e.g., high zones had high productivity potential while low zones had low productivity potential). Economic analysis was also performed. Based on grain yield productivity, the SSMZs performed better than the YBMZ technique in most cases. Grain yield and N uptake between the low and high productivity management zones were statistically different for most site-years and N fertilizer rates (p < 0.05). Soil properties helped to explain the productivity potential of the management zones. The low SSMZ was markedly different from the high SSMZ based on bulk density, organic carbon, sand, silt, porosity and soil moisture. Net returns ranged from 188 to 679 USD ha^?1. In two out of three site-years the variable yield goal strategy resulted in the largest net returns. In this study, the SSMZ approach delineates areas of different productivity accurately across the agricultural fields. The SSMZs are different with regard to soil properties as well as grain yield and N uptake. Site-specific management zones are an inexpensive and pragmatic approach to precise N management in irrigated maize.