M. P. Tuohy

The use of diffuse reflectance spectroscopy for in situ carbon and nitrogen analysis of pastoral soils

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.

Spectroradiometer data structuring, pre‐processing and analysis – an IT based approach

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.

Key Performance Indicators for Simulated Variable-Rate Irrigation of Variable Soils in Humid Regions

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.

Field radiometer with canopy pasture probe as a potential tool to estimate and map pasture biomass and mineral components: A case study in the Lake Taupo catchment, New Zealand

Abstract 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 prediction accuracy was shown in standing K, it may still be possible to make a quantitative prediction using CRDR and FDR (RPD > 2.2). The semivariograms parameter “range” of biomass was longer (58.7 m) than the ranges of the other parameters (10.6–17.4 m), suggesting that biomass values influenced neighbouring values of biomass over greater distances than the other pasture parameters (masses of N, P, K, and S).

Soil C and N sequestration and fertility development under land recently converted from plantation forest to pastoral farming

Abstract 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 sequestration could be used as an offset for forest C sink loss in future emissions trading systems. Further research is required to at least 0.3 m depth to confirm this preliminary study.

Potential for spectral indices to remotely sense phosphorus and potassium content of legume-based pasture as a means of assessing soil phosphorus and potassium fertility status

Precision nutrient management needs analytical tools that aid collection of site-specific data. Adequate soil phosphorus (P) and potassium (K) fertility is crucial for pasture production in New Zealand. This article explores (a) the relationship between 12 spectral indices from in situ canopy reflectance and pasture growth rate (PGR), and pasture P and K content in pastures, (b) the performance of the model in different seasons and (c) the relationship between sensed pasture P and K content and soil P (Olsen P) and K (exchangeable K) fertility. Hyperspectral data were collected from a small area of each of 30 legume-based pastures that varied in soil P (Olsen P 5–72 mg kg−1) and soil exchangeable K (0.20−1.32 cmol kg−1) in spring 2004 and again in summer 2006. Overall, the photochemical reflectance index (PRI) showed the best coefficients of determination (R 2) for most variables. In an exploratory analysis using all the spectral waveband data, normalized difference spectral indices (NDSIs) using the combination of reflectance at 523 and 583 nm of the pasture canopy gave the best prediction of soil P and exchangeable K status. The prediction of Olsen P from plant P (R 2 > 0.89) and soil K from plant K (R 2 > 0.73) was achieved through fitted logarithmic functions that linked plant P and K to soil P and K status, respectively. This pilot study has been broadened to examine other methodologies for interpreting the spectral data and extended to other pasture types and soil orders.

Seasonal prediction of in situ pasture macronutrients in New Zealand pastoral systems using hyperspectral data

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%).

Predicting Soil Carbon and Nitrogen Concentrations and Pasture Root Densities from Proximally Sensed Soil Spectral Reflectance

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).

Large, durable and low-cost reflectance standard for field remote sensing applications

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.

Proximal Sensing Methods for Mapping Soil Water Status in an Irrigated Maize Field

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.