This research studies the effect of stratifying soil samples to try and find a suitable depth to establish a geospatial relationship for a practical soil sampling grid in New Zealand hill country. Cores were collected from 200 predetermined sites in grids at two trial sites at "Patitapu" hill country farm in the Wairarapa, New Zealand. Trial 1 was a 200 m × 100 m grid located in a gently undulating paddock. Trial 2 was a 220 m × 80 m grid located on a moderately sloped paddock. Each grid had cores taken at intervals of 5 m, 10 m, or 20 m. Core sites were mapped out prior to going into the field; these points were found using a Leica Geo Systems GS15 (real time kinematic GPS) and marked with pigtail pegs and spray-paint on the ground. Cores were taken using a 50 mm-diameter soil core sampler. Cores were cut into three sections according to depth: A-0-30 mm, B-30-75 mm, and C-75-150 mm. Olsen P lab results were obtained for half of the total 1400 samples due to financial constraints. The results indicate that there was a significant decrease in variability from Section A to Section B for both trials. Section B and C for Trial 1 had similar variability, whereas there was another significant drop in variability from Section B to C in Trial 2. Measuring samples below the top 3 cm appeared to effectively reduce noise when sampled from 3 to 15 cm. However, measuring from 7.5 cm to 15 cm on the slope in Trial 2 reduced variability so much that all results were almost identical, which may mean that there is no measurable representation of plant available P. The reduction in noise by removing the top 3 cm of soil samples is significant for improving current soil nutrient testing methods by allowing better geospatial predictions for whole paddock soil nutrient variability mapping.

Spatial variability in soil, crop, and topographic features, combined with temporal variability between seasons can result in variable annual yield patterns within a paddock. The complexity of interactions between yield-limiting factors such as soil nutrients and soil water require specialist statistical processing to be able to quantify variability, and thus inform crop management practices. This study uses multiple linear regression models, Cubist regression and feed-forward neural networks to predict spatial maize-grain (Zea mays) yield at two sites in the Waikato Region, New Zealand. The variables considered were: crop reflectance data from satellite imagery, soil electrical conductivity, soil organic matter, elevation, rainfall, temperature, solar radiation, and seeding density. This exercise explores methods which may be useful in predicting yield from proximal and remote sensed data with higher resolution than traditional low spatial resolution point sampling using soil testing and yield response curves.

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Two polyester-lignite composite coated urea controlled-release fertilisers (CRFs; Poly3 and Poly5) were developed and their physicochemical properties were studied. Both these CRFs significantly (p < 0.05) extended the urea release compared to uncoated urea; Poly3 and Poly5 by 117 and 172 hours, respectively. The urea release characteristics of Poly5 were further enhanced by linseed oil application (Poly5-linseed). The SEM images demonstrated the coatings were in contact with the urea and encase urea particles completely with the average coating thickness of 167.2 ± 15 µm. The new interactions between polyester and lignite in the composite coating were confirmed by the FTIR analysis. Polyester-calcium carbonate (Polyester-CaCO3) coated CRFs (Calc3 & Calc5) were developed using CaCO3 as a filler in place of lignite and the urea dissolution rate was compared with Poly3 and Poly5. The urea release times for the polyester- CaCO3 formulations, 48h and 72h, were significantly (P < 0.05) lower than the polyester-lignite formulation, showing that lignite imparted greater control over release time than CaCO3. Findings from this work showed that polyester-lignite composites can be used as a coating material for CRFs.

Two polyester-lignite composite coated urea slow-release fertilizers (SRFs; Poly3 and Poly5) were developed and their physicochemical properties were studied. Both these SRFs significantly (p < 0.05) extended the urea release compared to uncoated urea; Poly3 and Poly5 by 117 and 172 h, respectively. The urea release characteristics of Poly5 were further enhanced by linseed oil application (Poly5-linseed). The SEM images demonstrated the coatings were in contact with the urea and encase urea particles completely with the average coating thickness of 167.2 ± 15 µm. The new interactions between polyester and lignite in the composite coating were confirmed by the FTIR analysis. Polyester-calcium carbonate (Polyester-CaCO3) coated SRFs (Calc3 and Calc5) were developed using CaCO3 as a filler in place of lignite and the urea dissolution rate was compared with Poly3 and Poly5. The urea release times for the polyester-CaCO3 formulations, 48 and 72 h, were significantly (P < 0.05) lower than the polyester-lignite formulation, showing that lignite imparted greater control over release time than CaCO3. Findings from this work showed that polyester-lignite composites can be used as a coating material for SRFs. Graphical abstract