Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming

Understanding spatial variability of indigenous nitrogen (N) supply (INS) is important to the implementation of precision N management (PNM) strategies in small scale agricultural fields of the North China Plain (NCP). This study was conducted to determine: (1) field-to-field and within-field variability in INS; (2) the potential savings in N fertilizers using PNM technologies; and (3) winter wheat (Triticum aestivum L.) N status variability at the Feekes 6 stage and the potential of using a chlorophyll meter (CM) and a GreenSeeker active crop canopy sensor for estimating in-season N requirements. Seven farmer’s fields in Quzhou County of Hebei Province were selected for this study, but no fertilizers were applied to these fields. The results indicated that INS varied significantly both within individual fields and across different fields, ranging from 33.4 to 268.4 kg ha⁻¹, with an average of 142.6 kg ha⁻¹ and a CV of 34%. The spatial dependence of INS, however, was not strong. Site-specific optimum N rates varied from 0 to 355 kg ha⁻¹ across the seven fields, with an average of 173 kg ha⁻¹ and a CV of 46%. Field-specific N management could save an average of 128 kg N ha⁻¹ compared to typical farmer practices. Both CM and GreenSeeker sensor readings were significantly related to crop N status and demand across different farmer’s fields, showing a good potential for in-season site-specific N management in small scale farming systems. More studies are needed to further evaluate these sensing technology-based PNM strategies in additional farmer fields in the NCP.     

QuickBird satellite versus ground-based multi-spectral data for estimating nitrogen status of irrigated maize

In-season nitrogen (N) management of irrigated maize (Zea mays L.) requires frequent acquisition of plant N status estimates to timely assess the onset of crop N deficiency and its spatial variability within a field. This study compared ground-based Exotech nadir-view sensor data and QuickBird satellite multi-spectral data to evaluate several green waveband vegetation indices to assess the N status of irrigated maize. It also sought to determine if QuickBird multi-spectral imagery could be used to develop plant N status maps as accurately as those produced by ground-based sensor systems. The green normalized difference vegetation index normalized to a reference area (NGNDVI) clustered the data for three clear-day data acquisitions between QuickBird and Exotech data producing slopes and intercepts statistically not different from 1 and 0, respectively, for the individual days as well as for the combined data. Comparisons of NGNDVI and the N Sufficiency Index produced good correlation coefficients that ranged from 0.91 to 0.95 for the V12 and V15 maize growth stages and their combined data. Nitrogen sufficiency maps based on the NGNDVI to indicate N sufficient (≥0.96) or N deficient (<0.96) maize were similar for the two sensor systems. A quantitative assessment of these N sufficiency maps for the V10–V15 crop growth stages ranged from 79 to 83% similarity based on areal agreement and moderate to substantial agreement based on the kappa statistics. Results from our study indicate that QuickBird satellite multi-spectral data can be used to assess irrigated maize N status at the V12 and later growth stages and its variability within a field for in-season N management. The NGNDVI compensated for large off-nadir and changing target azimuth view angles associated with frequent QuickBird acquisitions.     

Impact of Residual Soil Nitrate on In-Season Nitrogen Applications to Irrigated Corn Based on Remotely Sensed Assessments of Crop Nitrogen Status

Spatial and temporal variability of soil nitrogen (N) supply together with temporal variability of plant N demand make conventional N management difficult. This study was conducted to determine the impact of residual soil nitrate-N (NO₃-N) on ground-based remote sensing management of in-season N fertilizer applications for commercial center-pivot irrigated corn (Zea mays L.) in northeast Colorado. Wedge-shaped areas were established to facilitate fertigation with the center pivot in two areas of the field that had significantly different amounts of residual soil NO₃-N in the soil profile. One in-season fertigation (48 kg N ha⁻¹) was required in the Bijou loamy sand soil with high residual NO₃-N versus three in-season fertigations totaling 102 kg N ha⁻¹ in the Valentine fine sand soil with low residual NO₃-N. The farmer applied five fertigations to the field between the wedges for a total in-season N application of 214 kg N ha⁻¹. Nitrogen input was reduced by 78% and 52%, respectively, in these two areas compared to the farmer’s traditional practice without any reductions in corn yield. The ground-based remote sensing management of in-season applied N increased N use efficiency and significantly reduced residual soil NO₃-N (0–1.5 m depth) in the loamy sand soil area. Applying fertilizer N as needed by the crop and where needed in a field may reduce N inputs compared to traditional farmer accepted practices and improve in-season N management.     

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.     

Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages

Many hyperspectral vegetation indices (VIs) have been developed to estimate crop nitrogen (N) status at leaf and canopy levels. However, most of these indices have not been evaluated for estimating plant N concentration (PNC) of winter wheat (Triticum aestivum L.) at different growth stages using a common on-farm dataset. The objective of this study was to evaluate published VIs for estimating PNC of winter wheat in the North China Plain for different growth stages and years using data from both N experiments and farmers’ fields, and to identify alternative promising hyperspectral VIs through a thorough evaluation of all possible two band combinations in the range of 350–1075 nm. Three field experiments involving different winter wheat cultivars and 4–6 N rates were conducted with cooperative farmers from 2005 to 2007 in Shandong Province, China. Data from 69 farmers’ fields were also collected to evaluate further the published and newly identified hyperspectral VIs. The results indicated that best performing published and newly identified VIs could explain 51% (R₇₀₀/R₆₇₀) and 57% (R₄₁₈/R₄₀₅), respectively, of the variation in PNC at later growth stages (Feekes 8–10), but only 22% (modified chlorophyll absorption ratio index, MCARI) and 43% (R₇₆₃/R₇₆₁), respectively, at the early stages (Feekes 4–7). Red edge and near infrared (NIR) bands were more effective for PNC estimation at Feekes 4–7, but visible bands, especially ultraviolet, violet and blue bands, were more sensitive at Feekes 8–10. Across site-years, cultivars and growth stages, the combination of R₃₇₀ and R₄₀₀ as either simple ratio or a normalized difference index performed most consistently in both experimental (R ² = 0.58) and farmers’ fields (R ² = 0.51). We conclude that growth stage has a significant influence on the performance of different vegetation indices and the selection of sensitive wavelengths for PNC estimation, and new approaches need to be developed for monitoring N status at early growth stages.     

Within-field nitrogen response in corn related to aerial photograph color

Precise management of nitrogen (N) using canopy color in aerial imagery of corn (Zea mays L.) has been proposed as a strategy on which to base the rate of N fertilizer. The objective of this study was to evaluate the relationship between canopy color and yield response to N at the field scale. Six N response trials were conducted in 2000 and 2001 in fields with alluvial, claypan and deep loess soil types. Aerial images were taken with a 35-mm slide film from ≥1100 m at the mid- and late-vegetative corn growth stages and processed to extract green and red digital values. Color values of the control N (0 kg N ha⁻¹) and sufficient N (280 kg N ha⁻¹ applied at planting) treatments were used to calculate the relative ratio of unfertilized to fertilized and relative difference color values. Other N fertilizer treatments included side-dressed applications in increments of 56 kg N ha⁻¹. The economic optimal N rate was weakly related (R ² ≤ 0.34) or not related to the color indices at both growth stages. For many sites, delta yield (the increase in yield between control N and sufficient N treatments) was related to the color indices (R ² ≤ 0.67) at the late vegetative growth stage; the best relationship was with green relative difference. The results indicate the potential for color indices from aerial photographs to be used for predicting delta yield from which a site-specific N rate could be determined.     

Strategies to evaluate goodness of reference strips for in-season, field scale, irrigated corn nitrogen sufficiency

The nitrogen (N) sufficiency approach to assess plant N status for in-season N management requires a non-N-limiting reference to make N recommendations. Use of reference strips in fields with spatially variable soils and the impact this variability has within N enriched reference strips are not well understood. Consequently three strategies were investigated to evaluate the impact of spatially variable sandy soils within reference strips in two commercial center pivot-irrigated corn fields. Evaluation strategies were: (i) ignore soil spatial variability throughout the reference strips, (ii) account for soil variability in the reference strips based on second-order NRCS soil map units, and (iii) account for soil variability based on apparent electrical conductivity (EC_a) data as a surrogate for soil texture differences in the reference strips. A sufficiency index (SI) calculated from radiometer measured canopy reflectance data (SI_sensor) and from SPAD chlorophyll meter data (SI_meter) at two growth stages during corn vegetative growth were used to assess N sufficiency within the N enriched reference strips. By ignoring soil spatial variability in the reference strips, corn in the sandier soils was designated N deficient. Accounting for soil spatial variability using NRCS soil mapping units improved N sufficiency designations of corn in the reference strip for the different soil types contained within the reference strip but tended to designate corn in lighter texture areas within a mapping unit as N deficient. Use of EC_a as a surrogate for soil texture typically performed best for classifying corn N sufficiency throughout the reference strip and is recommended as a method to obtain reference strip normalizing values in fields with spatially variable sandy soils.     

Normalization of uncalibrated late-season digital aerial imagery for evaluating corn nitrogen status

Using uncalibrated digital aerial imagery (DAI) for diagnosing in-season nitrogen (N) status of corn (Zea mays L.) is challenging because of the dynamic nature of corn growth and the difficulty of obtaining timely imagery. Late-season DAI is more accurate for identifying areas deficient in N than early-season imagery. Even so, the quantitative use of the imagery across many fields is still limited because DAI is often not radiometrically calibrated. This study tested whether spectral characteristics of corn canopy derived from normalized uncalibrated late-season DAI could predict final corn N status. Color and near-infrared (NIR) imagery was collected in late August or early September across Iowa from 683 corn fields in 2006, 824 in 2007, and 828 fields in 2007. Four sampling areas (one within a target-deficient area) were selected within each field for conducting the end-of-season corn stalk nitrate test (CSNT). Each image was enhanced to increase the dynamic range within each field and to normalize reflectance values across all fields within a year. The reflectance values of individual bands and three vegetation indices were used to predict corn N status expressed as Deficient and Sufficient (a combination of marginal, optimal, and excessive CSNT categories) using a binary logistic regression (BLR). The green reflectance had the highest prediction rate, which was 70, 64, and 60% in 2006, 2007, and 2008, respectively. The results suggest that the normalized (enhanced) late-season uncalibrated DAI can be used to predict final corn N status in large-scale on-farm evaluation studies.     

Use of soil nitrogen parameters and texture for spatially-variable nitrogen fertilization

Recent studies have demonstrated the potential importance of using soil texture to modify fertilizer N recommendations. The objective of this study was to determine (i) if surface clay content can be used as an auxiliary variable for estimating spatial variability of soil NO₃–N, and (ii) if this information is useful for variable rate N fertilization of non-irrigated corn [Zea mays (L.)] in south central Texas, USA across years. A 64 ha corn field with variable soil type and N fertility level was used for this study during 2004–2007. Plant and surface and sub-surface soil samples were collected at different grid points and analyzed for yield, soil N parameters and texture. A uniform rate (UR) of 120 kg N ha⁻¹ in 2004 and variable rates (VAR) of 0, 60, 120, and 180 kg N ha⁻¹ in 2005 through 2007 were applied to different sites in the field. Distinct yield variation was observed over this time period. Yield and soil surface clay content and soil N parameters were strongly spatially structured. Corn grain yield was positively related to residual NO₃–N with depth and either negatively or positively related to clay content depending on precipitation. Residual NO₃–N to 0.60 and 0.90 m depths was more related to corn yield than from shallower depths. The relationship of clay content with soil NO₃–N was weak and not temporally stable. Yield response to N rate also varied temporally. Supply of available N with depth, soil texture and growing season precipitation determined proper N management for this field.     

Relationship between potassium chloride suppression of corn stalk rot and soil microorganism characteristics

Observations from a site-fixed field trial of 12 years in Jilin Province show that potassium chloride (KCl) application has a significant positive influence on corn stalk rot incidence. Incubation experiments were conducted to study the effects of KCl and soil extracts on the growth of Fusarium graminearum, the most common stalk rot fungi in this area, and the population changes in rhizosphere fungi, bacteria and actinomyces at different growth stages of corn. The results show that KCl addition to the potato dextrose agar (PDA) medium could not directly suppress Fusarium graminearum development. Soil extracts from soil samples taken from the field plots with and without KCl application affected Fusarium graminearum development, with soil extracts with KCl treatments suppressing Fusarium graminearum development more significantly, compared with that from the KCl-free treatment. These results indicate that soil extracts play a role in the interaction between corn and Fusarium graminearum. Long-term KCl application may increase the populations of rhizosphere fungi and actinomyces in the early growth stages, while there is no significant difference in the number of bacteria in rhizosphere among the treatments. Also, the populations of rhizosphere fungi are negatively correlated with the incidence of stalk rot in the early growth stages of corn. The sensitive infection stages of pathogen to corn consist of the stages when there is significant difference in the populations of rhizosphere fungi and actinomyces. The change of microorganism populations (especially fungi) in soil may be associated with the incidence decrease and is one of the mechanisms of KCl suppressing corn stalk rot. Translated from Plant Nutrition and Fertilizer Science, 2007, 13(2): 279–285 [译自: 植物营养与肥料学报]     

Broad-scale cruciferous weed patch classification in winter wheat using QuickBird imagery for in-season site-specific control

This article explores the potential use of multi-spectral high-spatial resolution QuickBird imagery to detect cruciferous weed patches in winter wheat fields. In the present study, research was conducted on six individual naturally infested fields (field-scale study: field area ranging between 3 and 52 ha) and on a QuickBird-segmented winter wheat image (broad-scale study: area covering approximately 263 winter wheat fields, approximately 2 656 ha) located in the province of Córdoba (southern Spain). To evaluate the feasibility of mapping cruciferous weed patches in both the field-scale and broad-scale studies, two supervised classification methods were used: the Maximum likelihood classifier (MLC) and vegetation indices. Then, the best classification methods were selected to develop in-season site-specific cruciferous weed patch treatment maps. The analysis showed that cruciferous weed patches were accurately discriminated in both field-scale and broad-scale scenarios. Thus, considering the broad-scale study, classification accuracies of 91.3 and 89.45 % were obtained using the MLC and blue/green (B/G) vegetation indices, respectively. The site-specific treatment maps obtained from the best classifiers indicated that there is a great potential for reducing herbicide use through in-season, cruciferous weed patch site-specific control on both a field-scale and broad-scale. For example, it can be determined that by applying site-specific treatment maps on a broad-scale, herbicide savings of 61.31 % for the no-treatment areas and 13.02 % for the low-dose herbicide areas were obtained.     

Spatial and Temporal Variability of Corn Grain Yield: Site-Specific Relationships of Biotic and Abiotic Factors

Inadequate information on factors affecting crop yield variability has contributed to the slow adoption of site-specific farming (SSF). This study was conducted to determine the effects of biotic and abiotic factors on the spatial and temporal variability of irrigated corn grain yields and to derive information useful for SSF. The effects of water (80% evapotranspiration (ET) and 50% ET), hybrid (drought-tolerant and -susceptible), elevation, soil index (SI)(texture), soil NO₃–N, arthropods, and diseases on corn grain yield were investigated at Halfway, TX on geo-referenced locations. Grain yields were influenced by interrelationships among biotic and abiotic factors. Grain yields were consistently high under high water treatment, at higher elevations, and on soils with high SI (high clay and silt). Soil NO₃–N increased grain yields when water was adequate. Management zones for variable rate fertilizer and water application should, therefore, be based on information on elevation, SI, and soil NO₃–N. The effects of arthropods, diseases, and crop stress (due to drought and N) on corn grain yield were unpredictable. Spider mite (Oligonychus pratensis) and common smut (Ustilago zeae) damage occurred under hot and dry conditions in 1998. Spider mite infestations were high in areas with high soil NO₃–N. Moderate air temperatures and high relative humidity in 1999 favored southwestern corn borer (Diatraea grandiosella) and common rust (Puccinia maydis) incidences. Knowledge of conditions that favor arthropods and diseases outbreak and crop stress can improve the efficiency of scouting and in-season management of SSF. Management of SSF can be improved when effects of biotic and abiotic factors on grain yield are integrated and evaluated as a system.     

Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha⁻¹ conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (EC_a), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m² resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low EC_a, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving EC_a, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha⁻¹ compared to the recommended uniform rate of 170 kg N ha⁻¹, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.     

Site-specific production functions for variable rate corn nitrogen fertilization

Specific recommendations for variable rate nitrogen (VRN) fertilization in corn (Zea mays L.) are required to realize the potential environmental and economic benefits of this technology. However, recommendations based on algorithms that consider the processes controlling crop response to nitrogen fertilizer (NF) within fields have not yet been developed. The objectives of this study were to develop site-specific corn yield production functions for VRN fertilization and to determine the site-specific variables controlling corn response to NF. The experiments were conducted on eight commercial production fields. Fields were divided into 13–20 sections composed of five plots. Each plot received one NF rate. Site-specific variables included primary and secondary terrain attributes, and the Illinois Soil Nitrogen Test (ISNT). Nitrogen fertilizer significantly increased corn yield and it interacted with at least one site-specific variable. The ISNT was the site-specific variable that interacted with NF in most fields where the CV of ISNT was larger than 10%. The parameter estimates indicate that ISNT had a positive effect on corn yield and that it reduced the response to NF. Terrain attributes also affected corn yield and its response to NF. In general, parameter estimates indicated that well drained areas (i.e. small specific catchment area, moderate slopes) had higher yields and responded less to NF than areas where water is expected to accumulate. These results indicate that terrain attributes as surrogates for soil water content and the ISNT as a measure of soil mineralizable nitrogen are site-specific characteristics that affect corn yield and its response to NF.     

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.     

Changes in yield classification in a soybean-rice rotation

For yield based site-specific management to be successful in fields with crop rotations, changes in management zones between crops must be determined. The study objectives were to determine if yield classes change between crops within a rotation and whether soil properties can predict the yield classes or the year-to-year changes. A percentile classification method was used to categorize yearly soybean (Glycine max) and rice (Oryza sativa) yield in two fields with soybean-rice-soybean rotations into low, medium and high yield classes. There was little agreement in yield classifications between years. Yield class based on soil properties was predicted accurately by linear discriminant analysis in Field 1 20–67% of the time and in Field 2 13–83% of the time. Predictions in Field 1 were based on soil available Mg and P, elevation and the deep soil apparent electrical conductivity (EC_a). Predictions in Field 2 were based on soil texture, soil available P, K and Mg, and pH. The linear discriminant analysis was also able to predict year-to-year changes in yield class. Changes in class in Field 1 could be predicted by total soil C and N, silt, and soil available Mg and P depending on the year. Soil texture, soil available P, K and Mg, total soil C and pH, elevation and deep soil EC_a predicted yield changes in Field 2 depending on the year. The results of this study indicate only limited success at management zone definition in a soybean-rice rotation. Further investigation is needed with other crop rotation sequences to verify the findings of this study.     

Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn

The efficiency of side-dressing, a more efficient of nitrogen application method than uniform application in either late Fall or early Spring, relies heavily on the capability of nitrogen deficiency detection on a sprayer. To determine the site-specific yield potential for corn, multi-spectral image analysis including aerial- and ground-based images has been used. Some acceptable calibration relationships between the multi-spectral reflectance and SPAD readings have been found from previous study. In sunny weather conditions there was a shadow in the image made by corn leaf itself. This research investigated the shadow effect on the image for detecting corn nitrogen deficiency based on corn canopy reflectance information. The results indicated that the reflectance of red channel in shadow area showed strong inverse correlation, so the vegetation index NDVI using red and NIR channels also showed strong correlation (R² = 77) compared to the whole leaf and bright area. And the reflectance (green and red) and vegetation index(G_NDVI, NDVI, and ratio) in shadow area showed more consistent correlations than others using these image analysis methods.     

Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII

Nitrogen (N) from farm fields is a source of pollution to fresh and marine waters. Modifying N fertilizer application rate and timing to consider the spatial and temporal variability in plant N requirements could reduce N losses from farmlands, resulting in improvements to surface water quality. In this study, the field-scale hydrologic and N simulation model DRAINMOD-NII was used to predict nitrate–N losses from fields planted in a corn-soybean rotation at Waseca, Minnesota, USA, over a 15-year period (2003–2017) for two fertilizer application treatments. The N fertilizer treatments simulated included a single uniform fertilizer application in the spring before planting and a variable rate N practice (VRN) where fertilizer was applied as a split pre-plant, side-dress application, based on in-season monitoring of plant N requirements to determine fertilizer rate. Measured discharge (2003–2008) and nitrate–N concentrations in subsurface drainage (2003–2008 and 2016–2017) at the site were used to calibrate discharge and nitrate–N losses in model simulations and validate model performance for uniform vs VRN fertilizer management. Measured nitrate–N concentrations in weekly samples were 13% lower for fields utilizing VRN versus a single spring application in 2016, and 18% lower in 2017. Model predictions of nitrate concentrations based on daily predictions of discharge accurately matched observed data for these years, predicting reductions of 23% and 19% for the years 2016 and 2017, respectively. The results of model simulation for the 15-year period indicated that changing the timing of fertilizer application from a single application to a VRN application could reduce annual N loads lost in drainage by 40%.

     

Use of corn height to improve the relationship between active optical sensor readings and yield estimates

Early in-season loss of N continues to be a problem in corn (Zea mays L.). One method to improve N use efficiency is fertilizing based on in-season crop foliage sensors. The objective of this study was to evaluate two ground-based, active-optical (GBAO) sensors and explore the use of corn height with sensor readings for improving relationships with corn yield. Two GBAO sensors (GreenSeeker^® (GS), Trimble, Sunnydale, CA, USA; and Holland Crop Circle (CC) ACS 470 Sensor^®, Holland Scientific, Lincoln, NE, USA) were used within 30 established corn N-rate trials in North Dakota at the V6 and V12 growth stages in 2011 and 2012. Corn height was recorded manually at the date of sensor data collection. At the V6 growth stage, the GS relationship to yield and the INSEY (in-season estimate of yield) value was improved when the sensor reading was multiplied times corn height. At the V12 stage, using the GS, the INSEY relationship with yield was also generally increased when height was considered. The CC-based red/near-infrared INSEY relationship with yield was similar to the GS INSEY. The CC-based red edge/near infrared INSEY relationship was increased with height only at the first sensor date, but not with the second. The second CC-based sensor–INSEY relationship with yield was maximized using sensor reading only. Segregating the 30 site data set into sites with high clay surface textures and sites with medium texture improved all INSEY relationships compared to pooling all sites. Relationships between INSEY and corn yield at no-till sites were significant at the V12 stage in the wetter 2011 growing season, but not at the V6 stage either year, nor at the V12 stage in the very dry 2012 season. In the high clay and medium textured soils at the V6 stage, corn height improved the relationship between INSEY and yield often enough to suggest that incorporating corn height into an algorithm for yield prediction would strengthen yield prediction, and thus improve N rate decisions.     

Soil spatial variability and site-specific fertilization maps in an apple orchard

In the present study, the spatial variability of some soil physical and chemical properties in a 0.8 ha apple orchard were studied. Sixty soil samples were taken from two sampling depths: 0–0.3 m and 0.3–0.6 m. The soil samples were analyzed for the following soil properties: soil texture, pH, cation exchange capacity and NO₃–N, NH₄–N, P, K, Na, Ca, Mg, Fe, Zn, Mn, Cu, B and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. The site-specific application map for N was created based on the amount of N that was removed from the soil with the yield of the previous year. By applying N site-specifically, 38% of N could be saved compared to uniform application.