Nutrient Management Zones for Citrus Based on Variation in Soil Properties and Tree Performance

Site-specific soil management can improve profitability and environmental protection of citrus groves having large spatial variation in soil and tree characteristics. The objectives of this study were to identify soil factors causing tree performance decline in a variable citrus grove, and to develop soil-specific management zones based on easily measured soil/tree parameters for variable rate applications of appropriate soil amendments. Selected soil properties at six profile depths (0–1.5 m), water table depth, ground conductivity, leaf chlorophyll index, leaf nutrients and normalized difference vegetation index were compared at 50 control points in a highly variable 45-ha citrus grove. Regression analysis indicated that 90% of spatial variation in tree growth, assessed by NDVI, was explained by average soil profile properties of organic matter, color, near-infrared reflectance, soil solution electrical conductivity, ground conductivity and water table depth. Regression results also showed that soil samples at the surface only (0–150 mm) explained 78% of NDVI variability with NIR and DTPA-extractable Fe. Excessive available copper in low soil organic matter areas of the grove apparently induced Fe deficiency, causing chlorotic foliage disorders and stunted tree growth. The semivariograms of selected variables showed a strong spatial dependence with large ranges (varied from 230 m to 255 m). This grove can be divided into different management zones on the basis of easily measured NDVI and/or soil organic matter for variable rate application of dolomite and chelated iron to improve tree performance.     

Delineating productivity zones in a citrus grove using citrus production,tree growth and temporally stable soil data

The productivity of a citrus grove with variation in tree growth was mapped to delineate zones of productivity based on several indicator properties. These properties were fruit yield, ultrasonically measured tree canopy volume, normalized difference vegetation index (NDVI), elevation and apparent electrical conductivity (EC_a). The spatial patterns of soil series, soil color and EC_a, and their correspondence with the variation in yield emphasized the importance of variation in the soil in differentiating the productivity of the grove. Citrus fruit yield was positively correlated with canopy volume, NDVI and EC_a, and yield was negatively correlated with elevation. Although all the properties were strongly correlated with yield and were able to explain the productivity of the grove, citrus tree canopy volume was most strongly correlated (r = 0.85) with yield, explaining 73% of its variation. Tree canopy volume was used to classify the citrus grove into five productivity zones termed as ‘very poor’, ‘poor’, ‘medium’, ‘good’ and ‘very good’ zones. The study showed that productivity of citrus groves can be mapped using various attributes that directly or indirectly affect citrus production. The productivity zones identified could be used successfully to plan soil sampling and characterize soil variation in new fields.     

Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem

The advent of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of ecological phenomena over large areas in applied insect ecology and pest management. The objective of this study was to use geostatistics to characterize the spatial structure and map the spatial variation of damage caused by the berry borer (Hypothenemus hampei) and leaf miner (Leucoptera coffeella) in a coffee agroecosystem planted with the cultivar Catuai Vermelho IAC-99. Infestations of berry borer and leaf miner were evaluated in fruits and leaves, respectively. The pests were monitored at 67 georeferenced points in an area of 6.6 ha in 2005, 2006 and 2007. Variograms estimated by the method of moments (MoM) and residual maximum likelihood REML were compared. The latter were generally better in terms of the kriging error coefficients. Spherical variograms estimated by REML for berry borer infestation in fruits had ranges of spatial dependence of 34.62–118.4 m and for the leaf miner they were 53.93–133.7 m. For models fitted by weighted ordinary least squares (OLS) to the MoM experimental variogram, the ranges varied between 37.22 and 68.67 m for the berry borer and 100 and 155.4 m for leaf miner infestation. The variogram model parameters were used with the data for ordinary kriging to map the spatial variation of coffee pests for different monitoring periods. If there was no suitable variogram, inverse distance weighting was used to map the variation. The maps enabled visualization of the intensity of infestation of the insect pests for the different periods evaluated.     

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.     

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially

Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO₃, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R ² values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined use of VNIRRS and multivariate geostatistics results in better spatial prediction of soil properties and enables a reduction in sampling and laboratory analyses.     

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.     

Spatial multivariate classification of an arable field into compact management zones based on past crop yields

Many farmers want to take advantage of modern technology to control fertilizer applications in large fields more precisely in smaller zones. We have investigated zonation on past yields by three forms of k-means clustering with geostatistical smoothing and compared the outcomes with yield response to added nitrogen. We had data on wheat yield for three years on 2187 10 × 10 m squares in a 29-ha field in the English midlands. Two of the methods grouped the squares by a ‘hard’ means technique, the first used their similarities which had been modified with their spatial covariance function and the second used their dissimilarities which had been modified with their variogram. The third method computed their memberships to fuzzy classes and smoothed the memberships with the variogram for final classification. The resulting spatially smoothed classifications were evaluated by the extent to which they responded differentially to applied nitrogen in a fourth year.The best compromise between spatial coherence and minimum variance seemed to be the smoothed fuzzy classification; it created reasonably compact zones that a well-equipped farmer might be able to manage. However, the nitrogen response functions were parallel, and it is unlikely that a farmer would benefit financially by fertilizing the zones separately. The soil map of the field also distinguished coherent compact zones, and these differed in the forms of the responses of their crops to fertilizer nitrogen. For this field, it might be more profitable to manage the nitrogen application according to soil type.     

Spatial assessment of the correlation of seeding depth with emergence and yield of corn

Germination conditions are determined by hydraulic, thermal and mechanical properties of the soils. In heterogeneous fields, the most favourable seeding depth varies spatially. To investigate the influence of seeding depth on emergence and grain yield of corn, corn was planted in depths of 40, 50, 60, 70, 80 and 90 mm in three experimental years (2006–2008). The apparent soil electrical conductivity was measured with an EM38. The apparent electrical conductivity was used as a proxy for soil texture, top-soil thickness, effective root zone thickness, soil water content and soil structure. The spatial dependencies among emergence, yield and apparent electrical conductivity were considered by including spatial models into the statistical analysis. The results showed significant correlations of the apparent soil electrical conductivity, of the experimental year, and of the seeding depth with the emergence of corn. Deeper planted corn (80 or 90 mm) resulted in more emergence than shallow planted corn (+4.4% in 2006, +1.2% in 2007 and +1.5% in 2008). The emergence decreased with increasing apparent soil electrical conductivity values. The corn grain yield was significantly affected by the soil electrical conductivity, by emergence and by the experimental year. Increasing apparent soil electrical conductivity values were correlated with decreasing yield (from 7.5 to 3.4 Mg ha⁻¹ in 2006, from 10.8 to 5.3 Mg ha⁻¹ in 2007 and from 8.4 to 2.9 Mg ha⁻¹ in 2008). Increasing emergence resulted in increasing yield.     

Inverse meta-modelling to estimate soil available water capacity at high spatial resolution across a farm

Geo-referenced information on crop production that is both spatially- and temporally-dense would be useful for management in precision agriculture (PA). Crop yield monitors provide spatially but not temporally dense information. Crop growth simulation modelling can provide temporal density, but traditionally fail on the spatial issue. The research described was motivated by the challenge of satisfying both the spatial and temporal data needs of PA. The methods presented depart from current crop modelling within PA by introducing meta-modelling in combination with inverse modelling to estimate site-specific soil properties. The soil properties are used to predict spatially- and temporally-dense crop yields. An inverse meta-model was derived from the agricultural production simulator (APSIM) using neural networks to estimate soil available water capacity (AWC) from available yield data. Maps of AWC with a resolution of 10 m were produced across a dryland grain farm in Australia. For certain years and fields, the estimates were useful for yield prediction with APSIM and multiple regression, whereas for others the results were disappointing. The estimates contain ‘implicit information’ about climate interactions with soil, crop and landscape that needs to be identified. Improvement of the meta-model with more AWC scenarios, more years of yield data, inclusion of additional variables and accounting for uncertainty are discussed. We concluded that it is worthwhile to pursue this approach as an efficient way of extracting soil physical information that exists within crop yield maps to create spatially- and temporally-dense datasets.     

Impact of variable-rate application of nitrogen on yield and profit: a case study from South Africa

The response of maize (Zea mays) to banded variable-rate nitrogen (N) application over a period of 3 years (2002/3–2004/5) is analyzed. The experimental design alternated variable-rate (VR) and single-rate (SR) applications of N. The yield monitor data were spatially autocorrelated and therefore were analyzed with spatial regression methods. The baseline spatial regression model defined in this study showed that the VR treatment, treatment by year and treatment by management zone were statistically significant. Sensitivity tests were applied; the first showed that VR treatment had a yield advantage when soil depth was greater than the field average of 174 cm. The second test showed that the VR N rates applied were close to those that would maximize profit. Partial budgeting indicates that benefits from VR vary from year to year, but in this test VR was slightly more profitable than uniform rate application. Economic sensitivity testing indicates that farm size and the price of maize are the key factors in the profitability of VR N.     

Spatial and temporal stability of soil phosphate concentration and pasture dry matter yield

Several methods are described that could be used by a farm manager to define the spatial and temporal stability within a field from a series of yield maps. A quantitative analysis of soil phosphate concentration and pasture dry matter yield data over 4 years (2004–2007) were investigated to identify the spatial and temporal stability in a 6 ha pasture field. The data were combined into two maps that characterize the spatial and temporal variation recorded over the 4 years. The two maps were then combined to create a single map with five management classes, each with different characteristics that can have an impact on the way the field is managed. These categories are: high yielding and stable, high yielding and moderately stable, low yielding and stable, low yielding and moderately stable and unstable. The unstable class represents 83 and 93% of the total area with regard to soil phosphate concentration and pasture dry matter yield, respectively. Results from this study show that the significant temporal stability found cancels out over time, leaving a relatively homogenous map of spatial variation. The implication of the findings is that each pasture field should be managed according to the current year’s conditions. These results also justify a further study that evaluates the soil phosphorous dynamics under Mediterranean conditions.     

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.     

Off-nadir hyperspectral measurements in maize to predict dry matter yield,protein content and metabolisable energy in total biomass

Sensor-based methods of analysis to assess dry matter yield and quality constituents of crops are time- and labour-saving, and can facilitate site-specific management. Nevertheless, standard nadir measurements of maize (Zea mays cv. Ambrosius), based on top-of-canopy reflectance, are difficult due to plant heights of more than three metres. This study was conducted to explore the potential of off-nadir field spectral measurements for the non-destructive prediction of dry matter yield (DM), metabolisable energy (ME) and crude protein (CP) in total biomass in a maize canopy. Plants were measured at five different heights (0–50, 50–100, 100–50, 150–200 and 200–250 cm above the soil) at three zenith view angles (60°, 75° and 90°, respectively). Modified partial least squares regression was used for analysis of the hyperspectral data (355–2300 nm and 620–1000 nm). Optimum combinations of angle and height as well as an optimum one-sensor-strategy were determined for DM yield, CP and ME in total biomass. Coefficients of determination for off-nadir measurements were compared to nadir measurements; the results showed improved prediction accuracies for DM yield and ME using off-nadir measurements, but not for CP for which nadir measurements were better.     

Spatial Variability of Agricultural Soil Fertility Parameters in a Gleyic Podzol of Belgium

Precise information about the spatial variability of soil nutrients is essential in developing site-specific management, e.g., variable rate application of nutrients. In this study 5 fields (4 grassland, 1 arable land) on a Gleyic Podzol, each of about 1 ha, were sampled according to a 20 m×20 m grid. On these samples pH, carbon content and nutrient status (P, K, Ca, Mg, and Na) were measured. The variogram analysis of the different soil fertility parameters revealed quite different ranges for these parameters. Carbon content, pH and calcium have high ranges, between 110 and 170 m. On the other hand, ranges for potassium and sodium are much smaller, 60 m and 30 m respectively. For the elements potassium and sodium, high coefficients of variation are found, especially on the grassland fields. These differences in ranges of spatial relationship indicate that the sampling strategy for estimating variability of soil fertility, should be adopted to the different selected soil parameters and the soil use.     

Estimation of economic and environmental potentials of variable rate versus uniform N fertilizer application to spring barley on morainic soils in SE Norway

Spring barley was grown for 4 years (2001–2004) in field trials at two sites on morainic soil in central SE Norway, with five N level treatments: 0, 60, 90, 120 and 150 kg N ha^-1. Regression analyses showed that a selection of soil properties could explain 95–98% of the spatial yield variation and 47–90% of the yield responses (averaged over years). A strategy with uniform fertilizer application of 120 kg N ha⁻¹ (U _N120) was compared with two variable-rate (VR) strategies, with a maximum N rate of either 150 kg N ha⁻¹ (VR_N150) or 180 kg N ha⁻¹ (VR_N180). These strategies were tested using either Norwegian prices (low price ratio of N fertilizer to yield value; P_N/P_Y), or Swedish prices (high P_N/P_Y). The VR_N180 strategy had the highest potential yield and net revenue (yield value minus N cost) at both sites and under both price regimes. Using this strategy with Norwegian prices would increase the profit of barley cropping as long as at least 40 and 31% of the estimated potential increase in net revenue was realized, respectively. Using Swedish prices, uniform application appeared to be as good as or even better economically than the VR methods, when correcting for extra costs of VR application. The environmental effect of VR compared with uniform application, expressed as N not accounted for, showed contrasting effects when using Norwegian prices, but was clearly favourable using Swedish prices, with up to 20% reduction in the amount of N not accounted for.     

Can Landform Stratification Improve Our Understanding of Crop Yield Variability?

Farmers account for yield and soil variability to optimize their production under mainly economic considerations using the technology of precision farming. Therefore, understanding of the spatial variation of crop yield and crop yield development within arable fields is important for spatially variable management. Our aim was to classify landform units based on a digital elevation model, and to identify their impact on biomass development. Yield components were measured by harvesting spring barley (Hordeum vulgare, L.) in 1999, and winter rye (Secale cereale, L.) in 2000 and 2001, respectively, at 192 sampling points in a field in Saxony, Germany. The field was stratified into four landform units, i.e., shoulder, backslope, footslope and level. At each landform unit, a characteristic yield development could be observed. Spring barley grain yields were highest at the level positions with 6.7 t ha⁻¹ and approximately 0.15 t ha⁻¹ below that at shoulder and footslope positions in 1999. In 2000, winter rye harvest exhibited a reduction at backslope positions of around 0.2 t ha⁻¹ as compared to the highest yield obtained again at level positions with 11.1 t ha⁻¹. The distribution of winter rye grain yield across the different landforms was completely different in 2001 from that observed in 2000. Winter rye showed the highest yields at shoulder positions with 11.1 t ha⁻¹, followed by the level position with 0.5 t ha⁻¹ less grain yield. Different developments throughout the years were assumed to be due to soil water and meteorological conditions, as well as management history. Generally, crop yield differences of up to 0.7 t ha⁻¹ were found between landform elements with appropriate consideration of the respective seasonal weather conditions. Landform analysis proved to be helpful in explaining variation in grain yield within the field between different years.     

Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals

Lack of automatic weed detection tools has hampered the adoption of site-specific weed control in cereals. An initial object-oriented algorithm for the automatic detection of broad-leaved weeds in cereals developed by SINTEF ICT (Oslo, Norway) was evaluated. The algorithm (“WeedFinder”) estimates total density and cover of broad-leaved weed seedlings in cereal fields from near-ground red–green–blue images. The ability of “WeedFinder” to predict ‘spray’/‘no spray’ decisions according to a previously suggested spray decision model for spring cereals was tested with images from two wheat fields sown with the normal row spacing of the region, 0.125 m. Applying the decision model as a simple look-up table, “WeedFinder” gave correct spray decisions in 65–85% of the test images. With discriminant analysis, corresponding mean rates were 84–90%. Future versions of “WeedFinder” must be more accurate and accommodate weed species recognition.     

Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize

Advances in precision agriculture technology have led to the development of ground-based active remote sensors that can determine normalized difference vegetation index (NDVI). Studies have shown that NDVI is highly related to leaf nitrogen (N) content in maize (Zea mays L.). Remotely sensed NDVI can provide valuable information regarding in-field N variability and significant relationships between sensor NDVI and maize grain yield have been reported. While numerous studies have been conducted using active sensors, none have focused on the comparative effectiveness of these sensors in maize under semi-arid irrigated field conditions. Therefore, the objectives of this study were (1) to determine the performance of two active remote sensors by determining each sensor’s NDVI relationship with maize N status and grain yield as driven by different N rates in a semi-arid irrigated environment and, (2) to determine if inclusion of ancillary soil or plant data (soil NO₃ concentration, leaf N concentration, SPAD chlorophyll and plant height) would affect these relationships. Results indicated that NDVI readings from both sensors had high r ² values with applied N rate and grain yield at the V12 and V14 maize growth stages. However, no single or multiple regression using soil or plant variables substantially increased the r ² over using NDVI alone. Overall, both sensors performed well in the determination of N variability in irrigated maize at the V12 and V14 growth stages and either sensor could be an important tool to aid precision N management.     

Using spectral distance, spectral angle and plant abundance derived from hyperspectral imagery to characterize crop yield variation

Vegetation indices (VIs) derived from remote sensing imagery are commonly used to quantify crop growth and yield variations. As hyperspectral imagery is becoming more available, the number of possible VIs that can be calculated is overwhelmingly large. The objectives of this study were to examine spectral distance, spectral angle and plant abundance (crop fractional cover estimated with spectral unmixing) derived from all the bands in hyperspectral imagery and compare them with eight widely used two-band and three-band VIs based on selected wavelengths for quantifying crop yield variability. Airborne 102-band hyperspectral images acquired at the peak development stage and yield monitor data collected from two grain sorghum fields were used. A total of 64 VI images were generated based on the eight VIs and selected wavelengths for each field in this study. Two spectral distance images, two spectral angle images and two abundance images were also created based on a pair of pure plant and soil reference spectra for each field. Correlation analysis with yield showed that the eight VIs with the selected wavelengths had r values of 0.73–0.79 for field 1 and 0.82–0.86 for field 2. Although all VIs provided similar correlations with yield, the modified soil-adjusted vegetation index (MSAVI) produced more consistent r values (0.77–0.79 for field 1 and 0.85–0.86 for field 2) among the selected bands. Spectral distance, spectral angle and plant abundance produced similar r values (0.76–0.78 for field 1 and 0.83–0.85 for field 2) to the best VIs. The results from this study suggest that either a VI (MSAVI) image based on one near-infrared band (800 or 825 nm) and one visible band (550 or 670 nm) or a plant abundance image based on a pair of pure plant and soil spectra can be used to estimate relative yield variation from a hyperspectral image.