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1.
We up-scaled the APSIM simulation model of crop growth, water and nitrogen dynamics to interpret and respond to spatial and temporal variations in soil, season and crop performance and improve yield and decrease nitrate leaching. Grain yields, drainage below the maximum root depth and nitrate leaching are strongly governed by interaction of plant available soil water storage capacity (PAWC), seasonal rainfall and nitrogen supply in the water-limited Mediterranean-type environment of Western Australia (WA). APSIM simulates the interaction of these key system parameters and the robustness of its simulations has been rigorously tested with the results of several field experiments covering a range of soil types and seasonal conditions in WA. We used yield maps, soil and weather data for farms at two locations in WA to determine spatial and temporal patterns of grain yield, drainage below the maximum root depth and nitrate leaching under a range of weather, soil and nitrogen management scenarios. On one farm, we up-scaled APSIM simulations across the whole farm using local weather and fertiliser use data and the average PAWC values of soil type polygons. On a 70 ha field on another farm, we used a linear regression of apparent soil electrical conductivity (ECa) measured by EM38 against PAWC to transform an ECa map of the field into a high resolution (5 m grid) PAWC map. We then used regressions of simulated yields, drainage below the maximum root depth and nitrate leaching on PAWC to upscale the APSIM simulations for a range of weather and fertiliser management scenarios. This continuous mapping approach overcame the weakness of the soil polygons approach, which assumed uniformity in soil properties and processes within soil type polygons. It identified areas at greatest financial and environmental risks across the field, which required focused management and simulated their response to management interventions. Splitting nitrogen applications increased simulated wheat yields at all sites across the field and decreased nitrate leaching particularly where the water storage capacity of the soil was small. Low water storage capacity resulted in both low wheat yields and large leaching loss. Another management option to decrease leaching may be to grow perennial vegetation that uses more water and loses less by drainage.Paper from the 5th European Conference on Precision Agriculture (5ECPA), Uppsala, Sweden, 2005  相似文献   

2.
Soil organic matter (SOM) is a key indicator of soil quality although, usually, detailed data for a given area is difficult to obtain at low cost. This study was conducted to evaluate the usefulness of soil apparent electrical conductivity (ECa), measured with an electromagnetic induction sensor, to improve the spatial estimation of SOM for site-specific soil management purposes. Apparent electrical conductivity was measured in a 10-ha prairie in NW Spain in November 2011. The ECa measurements were used to design a sampling scheme of 80 locations, at which soil samples were collected from 0 to 20 cm depth and from 20 cm to the boundary of the A horizon (ranging from 25 to 48 cm). The SOM values determined at the two depths considered were weighted to obtain the results for the entire A Horizon. SOM distribution maps were obtained by inverse distance weighting and geostatistical techniques: ordinary kriging (OK), cokriging (COK), regression kriging either with linear models (LM-RK) or with random forest (RF-RK). SOM ranged from 46.3 to 78.0 g kg?1, whereas ECa varied from 6.7 to 14.7 mS m?1. These two variables were significantly correlated (r = ?0.6, p < 0.05); hence, ECa was used as an ancillary variable for interpolating SOM. A strong spatial dependence was found for both SOM and ECa. The maps obtained exhibited a similar spatial pattern for SOM; COK maps did not show a significant improvement from OK predictions. However, RF-RK maps provided more accurate spatial estimates of SOM (error of predictions was between four and five times less than the other interpolators). This information is helpful for site-specific management purposes at this field.  相似文献   

3.
A four-year study was conducted from 2000 to 2004 at eight field sites in Montana, North Dakota and western Minnesota. Five of these sites were in North Dakota, two were in Montana and one was in Minnesota. The sites were diverse in their cropping systems. The objectives of the study were to (1) evaluate data from aerial photographs, satellite images, topographic maps, soil electrical conductivity (ECa) sensors and several years of yield to delineate field zones to represent residual soil nitrate and (2) determine whether the use of data from several such sources or from a single source is better to delineate nitrogen management zones by a weighted method of classification. Despite differences in climate and cropping, there were similarities in the effectiveness of delineation tools for developing meaningful residual soil nitrate zones. Topographic information was usually weighted the most because it produced zones that were more correlated to actual soil residual nitrate than any other source of data at all locations. The soil ECa sensor created better correlated zones at Minot, Williston and Oakes than at most eastern sites. Yield data for an individual year were sometimes useful, but a yield frequency map that combined several years of standardized yield data was more useful. Satellite imagery was better than aerial photographs at most locations. Topography, satellite imagery, yield frequency maps and soil ECa are useful data for delineating nutrient management zones across the region. Use of two or more sources of data resulted in zones with a stronger correlation with soil nitrate.  相似文献   

4.
Soil biological response to management is best evaluated in field-scale experiments within the context of the soil environment and crop; however, cost-effective methods are lacking to relate these data which span multiple spatial scales. We hypothesized that zones of apparent electrical conductivity (ECa) could be used to integrate soil properties (sampling-site scale), microbial-scale measures of vesicular-arbuscular mycorrhizal (VAM) fungi, and field-scale wheat yields from yield maps. An on-farm dryland experiment (250 ha) was established wherein two (32-ha) fields were assigned to each phase of a winter wheat (Triticum aestivum L.) – corn (Zea mays L.) – proso millet (Panicum miliaceum L.) – fallow rotation. Each field was mapped and classified into four zones (ranges) of ECa. Soil samples were collected from geo-referenced sites within ECa zones and analyzed for multiple soil properties associated with productivity (0–7.5 and/or 0–30 cm). Additionally, VAM fungi were assessed using C16:1(cis)11 fatty acid methyl ester biomarker (C16vam), glomalin immunoassay, and wet-aggregate stability (WAS) techniques (1–2mm aggregates from 0- to 7.5-cm soil samples). Concentrations of C16vam and WAS increased among cropping treatments as: fallow < wheat < corn < millet. Glomalin across crops and replicates, C16vam and WAS in fallow (crop effect removed), soil properties associated with productivity, and wheat yields were negatively correlated with ECa and different among ECa zones (P 0.05). Zones of ECa provide a point of reference for relating data collected at different scales. Monitoring cropping system parameters and profitability, over time, may allow linkage of microbial-scale processes to farm-scale economic and ecological outcomes.  相似文献   

5.
In production systems where high-resolution harvest data are unavailable there is often a reliance on ancillary information to generate potential management units. In these situations correct identification of relevant sources of data is important to minimize cost to the grower. For three fields in a sweet corn production system in central NSW, Australia, several sets of high-resolution data were obtained using soil and crop canopy sensors. Management units were derived by k-means classification for 2–5 classes using three approaches: (1) with soil data, (2) with crop data and (3) a combination of both soil and crop data. Crop quantity and quality were sampled manually, and the sample data were related to the different management units using multivariate analysis of variance (MANOVA). The corrected Akaike information criterion (AICc) was then used to rank the different sources of data and the different orders of management units. For irrigated, short-season sweet corn production the management units derived from the crop canopy sensor data explained more variation in key harvest variables than management units derived from an apparent soil electrical conductivity (ECa) survey or a mixture of crop and soil sensor data. Management units derived from crop data recorded just prior to side-dressing outperformed management units derived from data recorded earlier in the season. However, multi-temporal classification of early and mid-season crop data gave better results than single layer classification at any time. For all three fields in this study, a 3- or 4-unit classification gave the best results according to the information criterion (AICc). For growers interested in adopting differential management in irrigated sweet corn, investment in a crop canopy sensor will provide more useful high-resolution information than that in a high-resolution ECa survey.  相似文献   

6.
Continuous paddy rice cultivation requires fields to be flooded most of the time limiting seriously the collection of detailed soil information. So far, no appropriate soil sensor technology for identifying soil variability of flooded fields has been reported. Therefore, the primary objective was the development of a sensing system that can float, acquire and process detailed geo-referenced soil information within flooded fields. An additional objective was to determine whether the collected apparent electrical conductivity (ECa) information could be used to support soil management at a within-field level. A floating sensing system (FloSSy) was built to record ECa using the electromagnetic induction sensor EM38, which does not require physical contact with the soil. Its feasibility was tested in an alluvial paddy field of 2.7 ha located in the Brahmaputra floodplain of Bangladesh. The high-resolution (1 × 1 m) ECa data were classified into three classes using the fuzzy k-means classification method. The variation among the classes could be attributed to differences in subsoil (0.15–0.30 m below soil surface) bulk density, with the smallest ECa values representing the lowest bulk density. This effect was attributed to differences in compaction of the plough pan due to differential puddling. There was also a significant difference in rice yield among the ECa classes, with the smallest ECa values representing the lowest yield. It was concluded that the floating sensing system allowed the collection of relevant soil information, opening potential for precision agriculture practices in flooded crop fields.  相似文献   

7.
Site-specific soil and crop management will require rapid low-cost sensors that can generate position-referenced data that measure important soil properties that impact crop yields. Apparent electrical conductivity (ECa) is one such measure. Our main objective was to determine which commonly measured surface soil properties were related to ECa at six sites in the Texas Southern High Plains, USA. We used the Veris 3100 and Geonics EM-38 EC mapping systems on 12 to 47 ha areas in six center-pivot irrigation sites. Soil samples were taken from 0–150 mm on a 0.1 to 0.8 ha grid and analyzed for routine nutrients and particle size distribution. At four of the six sites, shallow ECa measured with the Veris 3100 (ECa-sh) positively correlated to clay content. Clay content was negatively related with ECa-sh at one site, possibly due to low bulk density of the shallow calcic horizon at that site. Other soil properties that were often correlated with ECa included soil extractable Ca2+, Mg2+, Na+, CEC, silt and soluble salts. Extractable K+, NO3–, SO4–, Mehlich-3-P, and pH were not related to ECa. Partial least squares regression (PLS) of seven soil properties explained an average of 61%, 51% and 37% of the variation in observed shallow ECa-sh, deep ECa with the Veris 3100 (ECa-dp) and ECa with the Geonics EM-38 (ECa-em), respectively. Including nugget, range and sill parameters from a spherical semivariance model of the residuals from PLS regression improved the fit of mixed models in 15 of 18 cases. Apparent EC, therefore can provide useful information to land-users about key soil properties such as clay content and extractable Ca2+, but that spatial covariance in these relationships should not be ignored.  相似文献   

8.
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9.
The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (ECa) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil ECa, three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil ECa and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best ECa linear regressions. Using ECa measurements, soil properties like extractable Na+ and Mg2+, clay and sand content were well estimated, while best estimates were obtained for extractable Na+ (r 2  = 0.770) and clay content (r 2  = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r 2  = 0.449). The methods described above provided maps of soil properties estimated by ECa in a GIS framework, and could save time and resources during vineyard establishment and management.  相似文献   

10.
Apparent soil electrical conductivity (ECa) has shown promise as a soil survey tool in the Midwestern United States, with a share of this interest coming from the precision agriculture community. To fully utilize the potential of ECa to map soils, a better understanding of temporal changes in ECa is needed. Therefore, this study was undertaken to compare temporal changes in soil ECa between different soils, to investigate the influence of changes in soil water content on soil ECa, and to explore the impacts these ECa changes might have on soil mapping applications. To this end, a 90 m long transect was established. Soil ECa readings were taken in the vertical and horizontal dipoles at five points once every one to two weeks from June until October in 1999 and 2000. At the same time, soil samples were collected to a depth of 0.9 m for volumetric soil water content analysis. Soil ECa readings were compared to soil water content. At four of the five sites linear regression analysis yielded r 2 values of 0.70 or higher. Regression line slopes tended to be greater in lower landscape positions indicating greater ECa changes with a given change in soil water content. Two of the soils had an ECa relationship that changed as the soils became dry. This is an item of concern if ECa is to be used in soil mapping. Results indicated that soil water content has a strong influence on the ECa of these soils, and that ECa has its greatest potential to differentiate between soils when the soils are moist. Soil water content is an important variable to know when conducting ECa surveys and should be recorded as a part of any report on ECa studies.  相似文献   

11.
The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (ECa) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the ECa in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the ECa patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the ECa and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the ECa data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.  相似文献   

12.
The goal of this research was to determine the potential for use of site-specific management of corn hybrids and plant densities in dryland landscapes of the Great Plains by determining (1) within-field yield variation, (2) yield response of different hybrids and plant densities to variability, and (3) landscape attributes associated with yield variation. This work was conducted on three adjacent fields in eastern Colorado during the 1997, -98, and -99 seasons. Treatments consisted of a combination of two hybrids (early and late maturity) and four plant densities (24,692, 37,037, 49,382 and 61,727 plants ha-1) seeded in replicated long strips. At maturity, yield was measured with a yield-mapping combine. Nine landscape attributes including elevation, slope, soil brightness (SB) (red, green, and blue bands of image), ECa (shallow and deep readings), pH, and soil organic matter (SOM) were also assessed. An analysis of treatment yields and landscape data, to assess for spatial dependency, along with semi variance analysis, and block kriging were used to produce kriged layers (10 m grids). Linear correlation and multiple linear regression analysis were used to determine associations between kriged average yields and landscape attributes. Yield monitor data revealed considerable variability in the three fields, with average yields ranging from 5.43 to 6.39 Mg ha-1 and CVs ranging from 20% to 29%. Hybrids responded similarly to field variation while plant densities responded differentially. Economically optimum plant densities changed by around 5000 plants ha-1 between high and low-yielding field areas, producing a potential savings in seed costs of $6.25 ha-1. Variability in yield across the three landscapes was highly associated with landscape attributes, especially elevation and SB, with various combinations of landscape attributes accounting for 47%, 95%, and 76% of the spatial variability in grain yields for the 1997, -98, and -99 sites, respectively. Our results suggest site-specific management of plant densities may be feasible.  相似文献   

13.
To resolve the spatial variation in soil properties intensively is expensive, but such knowledge is essential to manage the soil better and to achieve greater economic and environmental benefits. The objective of this study was to determine whether the soil apparent electrical conductivity (ECa), alone or combined with other variables, is a useful alternative for providing detailed information on the soil in the Extremadura region of Spain. Apparent soil electrical conductivity was measured and geographically weighted regression was used to characterize the spatial variation in soil properties, which in turn can be used for soil management. This study shows that soil cation exchange capacity, calcium content, clay percentage and pH have a relatively strong spatial correlation with ECa in the soil of the study area.  相似文献   

14.
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 (ECa). 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 ECa 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.  相似文献   

15.
A world-wide need to use water resources efficiently necessitates more effective approaches to study water and contaminant transport in soil. This study examined the effectiveness of a multi-receiver electromagnetic induction probe (Geonics EM31-3RT) and modeling software (EMIGMA) to delineate hydrological regimes at field scale. The site consisted of 20 (15 m × 15 m) tile-drained plots in Southern Ontario, Canada. Measurements of apparent soil electrical conductivity (ECa) and magnetic susceptibility were obtained using the EM31-3RT in each plot at four distances (0, 2.25, 4.5 and 7.5 m) from the tile drain, and on three occasions (August 22, 26 and 29) in 2003. The EMIGMA was used to simulate a depth profile of electrical conductivity (ECs) from EM31-3RT readings. The near-surface soil showed significantly (p < 0.01) smaller ECa values than at greater depth. The ECa measurements made directly over the tile drains were smaller than those observed further away due to the presence of the drains. Cluster analysis indicated that the largest ECa values were at the lower elevations of the site related to the redistribution of moisture from higher elevations. The effect of tile drains and rainfall events on ECa was simulated well by EMIGMA, with smaller ECs values above the drains compared to further away, and showing an increase in ECs in the near-surface soil after rain. This study suggests that EM31-3RT measurements combined with EMIGMA simulation of electrical conductivity can provide valuable information on depth profiles of ECa and water dynamics in soil.  相似文献   

16.
Rouze  Gregory  Neely  Haly  Morgan  Cristine  Kustas  William  Wiethorn  Matt 《Precision Agriculture》2021,22(6):1861-1889

Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or ECa). The purpose of this study, therefore, was to evaluate UAS imagery, relative to ECa, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (Tr), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and Tr layers were significant (P?<?0.01) predictors of canopy height. Additionally, NDVI and Tr maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P?=?0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than ECa data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given ECa alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P?<?0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

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17.

The study aims at spatial analysis of water deficit of fruit trees under semi-humid climate conditions. Differences of soil, root, and their relation with the spatial variability of crop evapotranspiration (ETa) were analyzed. Measurements took place in a six hectare apple orchard (Malus x domestica ‘Gala’) located in fruit production area of Brandenburg (latitude: 52.606°N, longitude: 13.817°E). Data of apparent soil electrical conductivity (ECa) in 25 cm were used for guided sampling of soil texture, bulk density, rooting depth, root water potential, and volumetric water content. Soil ECa showed high correlation with root depth. The readily available soil water content (RAW) was calculated considering three cases utilizing (i) uniform root depth of 1 m, (ii) measured values of root depth, and (iii) root water potential measured during full bloom, fruit cell division stage, at harvest. The RAW set the thresholds for irrigation. The ETa was calculated based on data from a weather station in the field and RAW cases in high, medium and low ECa conditions. ETa values obtained were utilized to quantify how fruit trees cope with spatial soil variability. The RAW-based irrigation thresholds for locations of low and high ECa value differed. The implementation of plant parameters (rooting depth, root water potential) in the water balance provided a more representative figure of water needs of fruit trees Consequently, the precise adjustment of irrigation including plant data can optimize the water use.

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18.
El-Naggar  A. G.  Hedley  C. B.  Roudier  P.  Horne  D.  Clothier  B. E. 《Precision Agriculture》2021,22(4):1045-1066

Soil water content (θ) measurement is vital for accurate irrigation scheduling. Electromagnetic induction surveys can be used to map spatial variability of θ when other soil properties are uniform. However, depth-specific θ variations, essential for precision irrigation management, have been less investigated using this method. A quasi-2-dimensional inversion model, capable of inverting apparent soil electrical conductivity (ECa) data to calculate estimates of true electrical conductivity (σ) down the entire soil profile, was developed using ECa data collected by a multi-coil Dualem-421S sensor. The optimal relationships between σ and volumetric water content (θv) were established using all coil arrays of the Dualem-421S, a damping factor of 0.04, an initial model of 35 mSm?1, and with ten iterations (R2?=?0.70, bias?=?0.00 cm3cm?3, RMSE?=?0.04 cm3cm?3). These relationships were then used to derive soil profile images of these properties, and as expected, θv and σ follow similar trends down the soil profile. The derived soil profile images for θv have potential use for irrigation scheduling to two ECa-derived soil management zones under a variable rate irrigation system at this case study site. They reflect the intrinsic soil differences that occur between texture, texture transitions and drainage characteristics. The method can also be used to guide placement of soil moisture sensors for in-season monitoring of spatio-temporal variations of θv. This soil imaging method showed good potential for predicting 2D depth profiles of soil texture, moisture and drainage characteristics, and supporting soil, plant and irrigation management.

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19.
Iron chlorosis can limit crop yield, especially on calcareous soil. Typical management for iron chlorosis includes the use of iron fertilizers or chlorosis tolerant cultivars. Calcareous and non-calcareous soil can be interspersed within fields. If chlorosis-prone areas within fields can be predicted accurately, site-specific use of iron fertilizers and chlorosis-tolerant cultivars might be more profitable than uniform management. In this study, the use of vegetation indices (VI) derived from aerial imagery, on-the-go measurement of soil pH and apparent soil electrical conductivity (ECa) were evaluated for their potential to delineate chlorosis management zones. The study was conducted at six sites in 2004 and 2005. There was a significant statistical relationship between grain yield and selected properties at two sites (sites 1 (2005) and 3), moderate relationships at sites 2 and 4, and weak relationships at site 5. For sites 1 (2005) and 3, and generally across all sites, yield was predicted best with the combination of NDVI and deep ECa. These two properties were used to delineate chlorosis management zones for all sites. Sites 1 and 3 showed a good relationship between delineated zones and the selected properties, and would be good candidates for site-specific chlorosis management. For site 5, differences in the properties between mapped zones were small, and the zones had weak relationships to yield. This site would be a poor candidate for site-specific chlorosis management. Based on this study, the delineation of chlorosis management zones from aerial imagery combined with soil ECa appears to be a useful tool for the site-specific management of iron chlorosis.  相似文献   

20.
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 (ECa) data as a surrogate for soil texture differences in the reference strips. A sufficiency index (SI) calculated from radiometer measured canopy reflectance data (SIsensor) and from SPAD chlorophyll meter data (SImeter) 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 ECa 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.  相似文献   

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