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Rapid land use change has taken place in many neighboring provinces of the capital of Vietnam such as Thai Nguyen province over the past 2 decades due to urbanization and industrialization. Deriving accurate and updated land cover and land-use change information is essential for the environmental monitoring, evaluation and management. In this study, a robust classification algorithm, Random Forest (RF) was employed in R programming to map and monitor temporal and spatial characteristics of urban expansion and land-use change in Thai Nguyen province, Vietnam. The results showed that there has been a substantial and uneven urban growth and a significant loss of forest and cropland between 2000 and 2016. Most of the conversion of agriculture and forest into built-up and mining uses were largely detected in rural regions and suburbs of Thai Nguyen. Further GIS analysis revealed that rapid urban and industrial expansion was spatially occurred in the southern rural portions and central area of the province. This study also demonstrates the potential of Landsat data and combination of R programming language and GIS to provide a timely, accurate and economical means to map and analyze temporal land cover and land use changes for future national and local land development planning. Keywords: Rural land cover, Remote sensing, GIS, R programming

Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, near-real-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R2=0.98; MCA6: R2=0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R2=0.63) and converted near-infrared (R2=0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera. Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.

To evaluate the ability of field remote sensing for predicting pasture macronutrients, hyperspectral reflectance data between 350 and 2500 nm were acquired from a number of dairy and sheep pasture canopies in New Zealand. Reflectance factor, absorbance, derivatives, and continuum-removal data were regressed against pasture nitrogen N, phosphorus P, and potassium K concentrations using partial least squares regression PLSR. Overall, more accurate predictions were achieved using the first derivative data. The accuracy of the PLSR calibration models to predict pasture N, P, and K concentrations increased with the separation of the pasture samples by season. Predictions with reasonable accuracy coefficient of determination, R 2 > 0.74, and the ratio of standard deviation SD of the nutrients measured to the root mean square error of cross-validation RMSECV ≥ 2.0 were obtained for N during winter RMSECV ≤ 0.23%, autumn RMSECV ≤ 0.36%, and summer RMSECV ≤ 0.43% seasons; P during autumn RMSECV = 0.05%; and K during summer RMSECV = 0.33%.

The nutritive value of pasture is an important determinant of the performance of grazing livestock. Proximal sensing of in situ pasture is a potential technique for rapid prediction of nutritive value. In this study, multispectral radiometry was used to obtain pasture spectral reflectance during different seasons (autumn, spring and summer) in 2009–2010 from commercial farms throughout New Zealand. The analytical data set (n = 420) was analysed to develop season-specific and combined models for predicting pasture nutritive-value parameters. The predicted parameters included crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, lignin, lipid, metabolizable energy (ME) and organic matter digestibility (OMD) using a partial least squares regression analysis. The calibration models were tested by internal and external validation. The results suggested that the global models can predict the pasture nutritive value parameters (CP, ADF, NDF, lignin, ME and OMD) with moderate accuracy (0·64 � r 2 � 0·70) while ash and lipid are poorly predicted (0·33 � r 2 � 0·40). However, the seasonspecific models improved the prediction accuracy, in autumn (0·73 � r 2 � 0·83) for CP, ADF, NDF and lignin; in spring (0·61 � r 2 � 0·78) for CP and ash; in summer (0·77 � r 2 � 0·80) for CP and ash, indi

Multi-spectral remote sensing of green vegetation provides an opportunity for assessing biophysical and biochemical properties. This technique could play a crucial role in pasture management by providing the means to evaluate pasture quality in situ. In this study, the potential of a 16-channel multi-spectral radiometer (MSR) for predicting pasture quality, crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) was evaluated. In situ canopy spectral reflectance was acquired from mixed pastures, under commercial farm conditions in New Zealand. The multi-spectral data were evaluated by single wavelength, linear and non-linear renormalized difference vegetation index (RDVI), and stepwise multiple linear regression (SMLR) models. The selected non-linear, exponential fit, RDVI index models described (0.65 ≤ r2 ≤ 0.85) of the variation of pasture quality components (CP, DCAD, ME and OMD), while CP, ash, DCAD, lipid, ME and OMD were estimated with moderate accuracy (0.60 ≤ r2 ≤ 0.80) by the SMLR model. The remaining pasture quality components ADF, NDF and lignin were poorly explained (0.40 ≤ r2 ≤ 0.58) by the models. This experiment concluded that the MSR has potential to rapidly estimate pasture quality in the field using non-destructive sampling.

A study was conducted to explore the potential use of a hand-held (proximal) hyperspectral sensor equipped with a canopy pasture probe to assess a number of pasture quality parameters: crude protein (CP), acid detergent fibre (ADF), neutral detergent fibre (NDF), ash, dietary cation–anion difference (DCAD), lignin, lipid, metabolisable energy (ME) and organic matter digestibility (OMD) during the autumn season 2009. Partial least squares regression was used to develop a relationship between each of these pasture quality parameters and spectral reflectance acquired in the 500–2 400 nm range. Overall, satisfactory results were produced with high coefficients of determination (R 2), Nash–Sutcliffe efficiency (NSE) and ratio prediction to deviation (RPD). High accuracy (low root mean square error-RMSE values) for pasture quality parameters such as CP, ADF, NDF, ash, DCAD, lignin, ME and OMD was achieved; although lipid was poorly predicted. These results suggest that in situ canopy reflectance can be used to predict the pasture quality in a timely fashion so as to assist farmers in their decision making.

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0–60 cm depth with a 4.6 cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the row of maize plants (rows were 60 cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5 cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r 2 = 0.75; ratio of prediction to deviation [RPD] = 2.03; root mean square error of cross-validation [RMSECV] = 1.68 mg/cm3), soil% C (r 2 = 0.86; RPD = 2.66; RMSECV = 0.48%) and soil% N (r 2 = 0.81; RPD = 2.32; RMSECV = 0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r = 0.60) or N (r = 0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.

Summary This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350–2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g −1 soil. The average root density in the Fluvial soil (13.21 mg g −1 ) was twice that in the Allophanic soil (6.88 mg g −1 ). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r 2 = 0.83, ratio prediction to deviation (RPD) = 2.44, root mean square error of cross-validation (RMSECV) = 1.96 mg g −1 ) than in the Fluvial soil (r 2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g −1 ). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change.

Precision farming requires data on resource status at a very fine, within‐paddock scale which is impractical to collect by traditional sampling methods. This paper demonstrates the potential of a field radiometer in conjunction with a canopy pasture probe (CAPP) and global positioning system (GPS) to predict and map the spatial distribution patterns of herbage biomass and mass of nutrients, such as nitrogen (N), phosphorous (P), potassium (K), and sulphur (S) in hill country grassland. The accuracy of the calibration model using partial least squares (PLS) regression was assessed by using coefficient of determination (R 2) and the ratio of prediction to standard deviation (RPD). Continuum‐removed derivative reflectance (CRDR) data used in a PLS model gave an excellent prediction of the standing masses of N, P, and S (R 2> 0.895, RPD > 3.0). Both first derivative reflectance (FDR) and CRDR datasets gave a good prediction of standing biomass (R 2 > 0.857, RPD > 2.5). Although relatively lower predi...

Soil organic matter accumulation and concomitant fertility changes in soils recently converted from plantation forest to pastoral agriculture in the Taupo‐Rotorua Volcanic Zone have been observed, with a probable soil C sequestration rate of 6.1 t ha‐1 year‐1, and a soil N sequestration rate of 0.451 ha‐1 year‐1, to 150 mm soil depth, for the first 5 years after conversion attwo of three selected farms. Rapid increases in Olsen P were observed, with soils reaching their optimum agronomic range within 3–5 years after conversion, at two of three farms. A decreasing C:N ratio with time since conversion reflects improved fertility status, and implies that in initial years of pasture establishment, N losses are reduced due to its immobilisation into soil organic matter. These research findings suggest that land‐use change from plantation forest to pastoral farm, with inputs of N, P, K and S to soils, allows significant soil C and N sequestration for at least 5 years after conversion. This rate of C se...

The development of the Canopy Pasture Probe (CAPP), for acquisition of in situ pasture canopy reflectance factor, required a suitable large reflectance standard. Spectralon® has been successfully used worldwide as a reflectance standard, but large panels (greater than 20 cm diameter) suitable for use with the CAPP are very expensive. In this context, a large, durable and low cost reflectance standard has been evaluated for use with an Analytical Spectral Devices FieldSpec® Pro FR spectroradiometer attached to the CAPP. In this study various ceramic tiles, barium sulphate powders, white paint and a Kodak card were tested. The material which best suited the requirements for a reflectance standard was the white ceramic tile sourced from Argentina. This tile produced around 80% total reflectance with reasonable uniformity over the spectral range analysed (400-2400 nm); and the vegetation spectra acquired using this tile as reference sample were very similar in shape to the vegetation spectra acquired using spectralon® as a white reference.

Precision farming requires data on resource status at a very fine, within-paddock scale which is impractical to collect by traditional sampling methods. This paper demonstrates the potential of a modified field hyperspectral radiometer (spectro-CAPP) to predict and map spatial distribution patterns of herbage biomass and standing mass of nitrogen (N), phosphorous (P), potassium (K), and sulphur (S) in a 2.8 ha paddock Keywords: geostatistical analysis, pasture quality, semivariogram, spatial distribution

Increasing the current precision of nutrient management will need analytical tools that aid in collecting site specific data. A technology with potential is hyperspectral remote sensing. Modern, portable spectroradiometers permit reflectance data in the spectral region between 350 and 2500 nm to be collected quickly. With the limited sampling, handling, and processing required the technology also offers rapid turn around times, if calibrations can be developed. In this paper the findings of a pilot study examining the use of hyperspectral reflectance spectra of pasture to indirectly assess the phosphorus (P) status of the soil are presented and discussed. Spectral data were collected in spring 2004 and again in summer 2006 from a small area of each of 30 legume-based sheep grazed pasture plots that varied in soil P fertility (Olsen P 6-68 μg/ml). Significant (P0.80). In an exploratory analysis using all the spectral waveband data, several paired-bands with high coefficients of determination (R2) were detected for pasture P and K content, but not for pasture N content, pasture growth rate or pasture dry matter content. The differences detected in pasture P content were consistent with the differences in soil P fertility measured by the Olsen P soil test, as indicated by the relationship between pasture P content and soil Olsen P in both 2004 (R2 = 0.90) and 2006 (R2 = 0.86). This pilot study needs to be broadened to examine other methodologies for interpreting the spectral data and extended to other pasture types and soil groups of varying soil fertility. Keywords: Olsen P, soil fertility, remote sensing, hyperspectral imaging, spatial variability, soil phosphate, plant phosphate

Unmanned Aerial Vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, near real time vegetation analysis. In order to guarantee quality scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be trialed against state of the art instruments. In the following study, four different types of optical UAV based sensors (RGB camera, near infrared camera, six band multispectral camera, and a high resolution spectrometer) were compared and validated in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data was collected in New Zealand over ryegrass pastures of various conditions. The UAV sensor data was validated with ground spectral measurements. It was found that large scale imaging of pasture variability can be achieved by either using a true color or a modified near infrared camera. A six band multispectral camera was used as an imaging spectrometer capable of identifying in field variations of vegetation status that correlate with ground spectral measurements. The high resolution spectrometer was validated and found to deliver spectral data that can match the quality of ground spectral measurements.

The Rakaia River and its tributaries have their source high in the Southern Alps but soon flow through broad valleys and then onto the Canterbury Plains. In these wide valleys the river channel is constantly changing as more rock is weathered and eroded from the mountains to maintain the supply of gravels to the system. Beginning with a Landsat TM image from 1989 a succession of satellite images (ASTER, SPOT and Worldview 2) has been analyzed to provide quantitative data that can be interpreted in terms of river dynamics. Within the permanent, well-defined high banks, the river channel, gravels and more persistent islands have been identified for different reaches in the Upper Rakaia. Changes in the distribution of the gravels within these various reaches have been related to the geomorphologic characteristics of the associated sub-catchments.

Approximately 80% of allocated freshwater in New Zealand is used for irrigation, and the area irrigated has increased by 55% every decade since 1965. The research described in this chapter therefore focuses on developing new techniques to map and monitor soil attributes relevant to irrigation water use efficiency. The apparent electrical conductivity (ECa) of soils under a 33-ha irrigated maize crop was mapped using a mobile electromagnetic induction (EM) and RTK-DGPS system, and this map was used to select three contrasting zones. Within each zone, further ECa values were recorded at a range of volumetric soil water contents (θ) to develop a relationship between ECa, soil texture, soil moisture, and available water-holding capacity (AWC) (R 2 = 0.8). This allowed spatial prediction of AWC, showing that these sandy and silty soils had similar AWCs (∼160 mm/m). High-resolution digital elevation data obtained in the EM survey were also co-kriged with TDR-derived θ to produce soil moisture prediction surfaces, indicating drying patterns and their relationship to topography and soil texture. There was a 12.5–13.1% difference in soil moisture to 45 cm soil depth between the wettest and the driest sites at any one time (n = 47). Spatial and temporal variability of soil moisture, indicated by these co-kriged prediction surfaces, highlights the need for a rapid high-resolution method to assess in situ soil moisture. The potential of soil spectral reflectance (350–2,500 nm range; 1.4–2 nm resolution) for rapid field estimation of soil moisture was therefore investigated. Soil spectra were pre-processed and regressed against known soil moisture values using partial least squares regression (R 2 calibration = 0.79; R 2 prediction using leave-one-out cross-validation = 0.71). These proximal sensing methods facilitate spatial prediction of soil moisture, information which could then be uploaded to a variable rate irrigator.

A modified soil probe for a portable spectroradiometer (ASD FieldSpecPro, Boulder, CO) was developed to acquire reflectance spectra (350–2,500 nm) from flat-sectioned horizontal (H method) soil surfaces of soil cores or from the vertical side (V method) of cylindrical soil cores. The spectra have been used to successfully predict soil carbon (C) and nitrogen (N) concentrations and root density. Partial least squares regression (PLSR) of the first derivative of the 5 nm space spectral data from method H against laboratory determined soil C and N concentrations produced calibrations that allowed quantitative estimates of C and N concentrations in unknown Pumice, Allophanic, and Tephric Recent soil samples (for C: R 2 validation = 0.76, RPD = 1.97; for N: R 2 validation = 0.84, RPD = 2.45). Compared to the H method, spectra acquired by the V method gave slightly more accurate predictions of soil C and N concentrations in Fluvial Recent soil (for C: R 2cross-validation (cv) = 0.95 and 0.97, RPD = 4.45 and 5.80; for N: R 2cross-validation = 0.94 and 0.96, RPD = 4.25 and 5.17, where the two values are for the H and V methods, respectively). Spectra acquired by the V method from drier soils in May produced a calibration against soil C and N concentrations that was capable of accurately predicting the soil C and N concentrations from spectra collected from wetter soils in November (C: R 2 validation = 0.97 and RPD = 3.43; for N: R 2 validation = 0.95 and RPD = 3.44). This indicates that a calibration dataset can have temporal robustness, which may reduce the number of calibrations that have to be performed. The root density predictions from spectra acquired by the H method were more accurate if soil types were separated into Allophanic soil (RPD = 2.42; R 2 cross-validation = 0.83) and Fluvial Recent soil (RPD = 1.99; R 2 cross-validation = 0.75).

This paper reports the use of visible/near-infrared reflectance spectroscopy (Vis-NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350–2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46–5.02 mg dry root cm−3) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76–5.02 mg dry root cm−3) than pots of the Ramiha soil (0.46–3.84 mg dry root cm−3). Each soil had visually distinct reflectance spectra in the range 470–2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r2 cross-validation = 0.78, ratio of prediction to deviation (RPD) = 2.14, root mean squares error of cross-validation (RMSECV) = 0.60 mg cm−3; with removing outliers, r2 cross-validation = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm−3). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.

Hyperspectral data collection results in huge datasets that need pre‐processing prior to analysis. A review of the pre‐processing techniques identified repetitive procedures with consequently a high potential for automation. Data from different hyperspectral field studies were collected and subsequently used as test sets for the described system. A relational database was utilized to store hyperspectral data in a structured way. Software was written to provide a graphical user interface to the database, pre‐processing and analysis functionality. The resulting system provides excellent services in terms of organised data storage, easy data retrieval and efficient pre‐processing. It is suggested that the use of such a system can improve the productivity of researchers significantly.

A field method has been developed for rapid in situ assessment of soil carbon (C) and nitrogen (N) content using a portable spectroradiometer (ASD FieldSpecPro). The technique was evaluated at 7 field sites in permanent pasture, and in 1-year, 3-year, and 5-year pine-to-pasture conversions on Pumice, Allophanic, and Tephric Recent Soils in the Taupo and Rotorua region of New Zealand. A total of 210 samples were collected from 2 depths: 37.5 and 112.5 mm. Field measurement of diffuse spectral reflectance was recorded from a flat sectioned horizontal soil surface of a soil core using a purpose-built contact probe attached by fibre optic cable to the spectroradiometer. A 15-mm soil slice was collected from each cut surface for analysis of total C and N using a LECO Analyser. Soils had a wide range of total C and N (0.26–11.21% C, 0.02–1.01% N). Partial least-squares regression analysis was used to develop calibration models between smoothed-first derivative 5-nm-spaced spectral data and LECO-measured total C and N. The models successfully predicted total C and N in the validation sets with the best prediction for C (RPD 2.01, r2 0.75, RMSEP 1.21%) and N (RPD 2.66, r2 0.86, RMSEP 0.07%). Prediction accuracy using different selection methods of calibration and validation set is reported. This study indicates that in situ assessment of soil C and N by field spectroscopy has considerable potential for spatially rapid measurement of soil C and N in the landscape.

Decision support tools for precise irrigation scheduling are required to improve the efficiency of irrigation water use globally. This article presents a method for mapping soil variability and relating it to soil hydraulic properties so that soil management zones for variable-rate irrigation can be defined. A soil-water balance is used to schedule hypothetical irrigation events based on one blanket application of water to eliminate plant stress (uniform rate irrigation, or URI) and compares this to variable-rate irrigation (VRI), where irrigation is tailored to specific soil zone available water-holding capacity (TAWC) values. The key performance indicators, i.e., irrigation water use, drainage water loss, nitrogen leaching, energy use, irrigation water use efficiency (IWUE), and virtual water content, are used to compare URI and VRI at three contrasting sites using four years of climate data for a dairy pasture and maize crop and two years of climate data for a potato crop. Our research found that VRI saved 9% to 19% irrigation water, with accompanying energy saving. Loss of water by drainage, during the period of irrigation, was also reduced by 25% to 45% using VRI, which reduced the risk of nitrogen leaching. Virtual water content of these three primary products further illustrates potential benefits of VRI and shows that virtual water content of potato production used the least water per unit of dry matter production.

Decision support tools for precise irrigation scheduling are required to improve the efficiency of irrigation water use globally. This paper presents a method for mapping soil variability and relating it to soil hydraulic properties so that soil management zones for variable rate irrigation can be defined. A soil water balance is used to schedule hypothetical irrigation events based on (i) one blanket application of water to eliminate plant stress, (uniform rate irrigation, URI) and compares this to (ii) variable rate irrigation (VRI), where irrigation is tailored to specific soil zone available water holding capacity (AWC) values. The key performance indicators: irrigation water use, drainage water loss, nitrogen leaching, energy use, irrigation water use efficiency (IWUE) and virtual water content are used to compare URI and VRI at three contrasting sites using four years climate data for a dairy pasture and maize crop and two years climate data for a potato crop. Our research found that VRI saved 9 - 19 % irrigation water, with accompanying energy saving. Loss of water by drainage was also reduced by 25 - 45% using VRI, during the period of irrigation, which reduced the risk of nitrogen leaching. Virtual water content of these three primary products further illustrates benefits of VRI and shows that virtual water content of potato production used least water per unit of dry matter production.