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1.
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   

2.
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   

3.
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   

4.
Every growing season, paddy fields are kept both flooded and drained for a significant period of time. As a consequence, these soils develop distinct physico-chemical characteristics. For practical reasons, these soils are mostly sampled under dry conditions, but the question arises how representative the results are for the wet growing conditions. Therefore, the apparent electrical conductivity (ECa) of a 1.4 ha alluvial paddy field located in the Brahmaputra floodplain of Bangladesh was measured in both dry and wet conditions by a sensing system using the electromagnetic induction sensor EM38, which does not require physical contact with the soil, and compared both surveys. Due to the smooth water surface under wet conditions which ensured increased stability of the sensing platform, the results of the survey showed considerably reduced micro-scale variability of ECa. Furthermore, the wet survey results more reliably furnished soil-related information mainly due to the absence of soil moisture dynamics. The differences between ECa under wet and dry conditions were attributed to differences in soil texture, mainly the sand content variation having considerable effect on soil moisture differences when flooded following drainage. Accordingly, the largest differences between ECa under wet and dry conditions were found in those parts of the field with a large sand content. Hence, the conclusion was that an ECa survey on flooded fields has an added value to precision soil management.  相似文献   

5.
Electromagnetic induction sensors, such as EM38, are used widely for monitoring and mapping soil attributes via the apparent electrical conductivity (ECa) of the soil. The sensor response is the depth-integrated combination of the depth-response function of the EM38 and ‘local’ electrical conductivity (ECaz) at depth. In deep, Vertosol soils, assuming the instrument depth-response function is not perturbed by the soil and where volumetric moisture content at depth (θv(z)) dominates ECaz, EM38 should be capable of predicting average moisture content without recourse to mathematically complicated, and unstable profile inversion processes. Firstly a multi-height EM38 experiment was conducted over deep Vertosol soils to confirm the veracity of the EM38 depth-response function and test the concomitant hypothesis of the EM38 response being an integrated (i.e. additive) combination of depth-response function and θv(z). Secondly, depth profiles of moisture content were used to calibrate the EM38 to infer average θv(z) within the ‘root-zone’ of crop plants—here taken to be surface—0.8 m and surface—1.2 m. EM38 calibration was performed using soil samples acquired from both extracted cores and excavated pits. Mathematical summation of measured θv(z) from sectioned cores and the known depth-response function of the EM38 was found to explain 99% and 97% of the variance in measured ECa for horizontal and vertical dipole configurations at multiple sensor heights above the ground. Average θv from surface to 0.8 m () and surface to 1.2 m () explained only 37% and 46% of the variance in on-ground ECa for vertical dipole configuration measurements compared to 55% and 56% of the variance for horizontal dipole configuration. In a separate validation experiment, the shape of the vertical moisture profile proved highly influential in determining the ability of the calibration equations to infer underlying average moisture content, especially where the depth profile shapes differed between sensor calibration and subsequent field validation (for example following rainfall or irrigation).  相似文献   

6.
Soil electrical conductivity (ECa) measured by electromagnetic induction (EM) using the EM-38 has shown promise as a soil survey tool. Soil temperature influences ECa readings, and temperature can fluctuate considerably in the upper 10cm of the soil during a day. ECa readings were taken in the horizontal and vertical dipole orientations once an hour from 8a.m. to 8p.m. at four sites on three separate days to determine if ECa values were influenced by diurnal temperature variations. Soil temperature readings were taken at the same times at four depths. EM-38 readings remained steady at all four sites all 3days. Linear regression analysis when temperature in the upper 10cm was plotted against ECa yielded low r 2 values and slopes, indicating no correlation between soil temperature in the upper 10cm and ECa values. Diurnal changes in soil temperature do not significantly influence soil ECa readings obtained with the EM-38 under the conditions encountered during the study.  相似文献   

7.
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   

8.
Domsch  H.  Giebel  A. 《Precision Agriculture》2004,5(4):389-409
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9.
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 (ECa). The spatial patterns of soil series, soil color and ECa, 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 ECa, 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.  相似文献   

10.
Management decisions, such as subsoil liming or varying fertilizer inputs to take account of soil depth and anticipated yields require knowledge of where subsoil constraints to root growth occur across the field. We used selected yield maps based on criteria derived from crop simulation, apparent soil electrical conductivity (ECa), gamma-ray emission maps and a soil type map drawn by the grower to predict the spatial distribution of subsoil acidity and shallow soil across a field. Yield maps integrate the effects of variation in soil and climate, and it was only under specific seasonal conditions that subsoil constraints depressed yields. We used crop simulation modelling to select yield maps with a large information content on the spatial distribution of these constraints and to omit those with potentially misleading information. Yield and other spatial data layers were used alone or in combination to develop subsoil mapping options to accommodate differences in data availability, access to precision agriculture techniques and the grower’s aptitude and preference. One option used gamma-ray spectrometry and EM38 survey as a dual-sensing system to improve data interpretation. Gamma-ray spectrometry helped to overcome the inability of current ECa-based methods to sense soil depth in highly weathered sandy soil over cemented gravel. A feature of the approaches presented here is the use of grower and agronomist knowledge, and experience to help interpret the spatial data layers and to evaluate which approach is most suitable and likely to be adopted to suit an individual.  相似文献   

11.
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   

12.
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 (ECa), 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 ECa, 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 ECa, 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.  相似文献   

13.
The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (ECa) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = ?0.64 and R = ?0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with ECa and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of ECa patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.  相似文献   

14.
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   

15.
土壤盐渍化问题是制约干旱半干旱区植被生长最主要的生态环境地质问题,也是影响绿洲农业生产的障碍性问题。而将遥感与近感技术相结合,是当前评价、监测及预报土壤盐渍化程度的先进方法。以新疆塔里木盆地北缘的渭干河-库车河三角洲绿洲为例,以遥感数据和解译后的电磁感应数据为基础数据源,利用解译后的数据结合GIS和地统计学知识以及野外实测所得到的土壤电导率和盐分资料,分别采用泛克里格(Universal Kriging)、光谱指数回归(Spectral Index Regression)和回归残差泛克里格(Regression-Universal Kriging)3种方法研究了该地区两个关键季节(干季和湿季)土壤盐分的空间变异特征。研究结果表明:研究区的土壤浸提液电导率EC1:5和土壤盐分呈现显著相关,可以用EC1:5来代替土壤的全盐量进行分析;电磁感应仪(EM38)所测各季节土壤表观电导率与EC1:5的相关系数均达到1%显著水平,以表观电导率垂直读数(EMV) 和水平读数(EMH)为自变量的多元回归模型拟合效果较好;研究区各季节的表层土壤电导率的空间分布均表现为强相关性,说明土壤采样点间的内部结构性良好,采用能够充分考虑到干旱区表层土壤电导率空间变异的尺度依赖性的球状套合模型,能够更好的拟合土壤表观电导率的空间结构;经过精度比较,回归残差泛克里格法为最优预测方法,这表明将遥感和电磁感应技术相结合,能够有效的提高预测与评估土壤盐分空间分布的精度,为精确地进行土壤盐分预测以及土壤次生盐渍化的防控提供了一定的依据。  相似文献   

16.
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.  相似文献   

17.
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   

18.
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   

19.
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   

20.

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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