Developing variable rate application technology: Economic impact for farm owners and topdressing operators

Murray, R. I.; Yule, Ian

doi:10.1080/00288230709510283

Cited by 15 publications

(16 citation statements)

References 8 publications

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“…They reported that the annual pasture production could be increased between 6.5-24.4% by VRF. They also conducted an economic analysis on implementing VRF over the blanket application where they found that this technique could increase 26% annual cash returns per hectare [45]. These findings clearly indicate the potential to improve fertilizer use efficiency and the economic benefits, as well as to reduce the risk of fertilizer wastage contaminating the environment from VRF [38].…”

Section: Discussionmentioning

confidence: 99%

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

2018

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Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R 2 CV (cross-validated coefficient of determination) = 0.70, RMSE CV (cross-validated root mean square error) = 2.06%, RPD CV (cross-validated ratio to prediction deviation) = 1.82 and ME: R 2 CV = 0.75, RMSE CV = 0.65 MJ/kg DM, RPD CV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R 2 CV = 0.80, RMSE CV = 1.68%, RPD CV = 2.23; ME: R 2 CV = 0.78, RMSE CV = 0.61 MJ/kg DM, RPD CV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits.

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Section: Discussionmentioning

confidence: 99%

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

2018

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“…GPS mapping and guidance ‘Autosteer’, yield mapping, robotic milking, RFID tagging, electromagnetic sensor mapping). The findings of this review led to further PA research in variable rate fertiliser application to hill country by top‐dressing and to flat land by ground spreading…”

Section: Early Studies Scoping the Potential Application Of Precisionmentioning

confidence: 92%

“…A study was conducted to investigate the potential use of precision technologies by hill country farming communities . It assessed the potential benefits from automating fertiliser flow control from top‐dressing planes to vary application rates based on the potential outputs of the farmland.…”

Section: Precision Agriculture At the Landscape Scale: Assessing Vratmentioning

confidence: 99%

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The role of precision agriculture for improved nutrient management on farms

Hedley

2014

J. Sci. Food Agric.

146

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Precision agriculture uses proximal and remote sensor surveys to delineate and monitor within-field variations in soil and crop attributes, guiding variable rate control of inputs, so that in-season management can be responsive, e.g. matching strategic nitrogen fertiliser application to site-specific field conditions. It has the potential to improve production and nutrient use efficiency, ensuring that nutrients do not leach from or accumulate in excessive concentrations in parts of the field, which creates environmental problems. The discipline emerged in the 1980s with the advent of affordable geographic positioning systems (GPS), and has further developed with access to an array of affordable soil and crop sensors, improved computer power and software, and equipment with precision application control, e.g. variable rate fertiliser and irrigation systems. Precision agriculture focusses on improving nutrient use efficiency at the appropriate scale requiring (1) appropriate decision support systems (e.g. digital prescription maps), and (2) equipment capable of varying application at these different scales, e.g. the footprint of a one-irrigation sprinkler or a fertiliser top-dressing aircraft. This article reviews the rapid development of this discipline, and uses New Zealand as a case study example, as it is a country where agriculture drives economic growth. Here, the high yield potentials on often young, variable soils provide opportunities for effective financial return from investment in these new technologies.

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“…with high slope). Such considerations would be of value in implementing the variable fertiliser rate approach advocated by Murray & Yule (2007). Additional factors to be considered in such exercises include the ease of management of land management units as influenced by topography and spatial location; the versatility of the soils; and the nature of the livestock enterprises that can be run on the units.…”

Section: Fertiliser Treatments and Pasture Productionmentioning

confidence: 98%

Responses of grazed New Zealand hill pastures to rates of superphosphate application

Lambert

Mackay

Ganesh

et al. 2014

New Zealand Journal of Agricultural Research

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Effects of superphosphate application rates on pasture production were measured between 1975 and 1988 within 12 grazed farmlets on steep hill country at the Ballantrae Hill Country Research Station in southern Hawke's Bay. The low fertiliser (LF) treatment received 125 kg ha −1 superphosphate pa, and the high fertiliser (HF) an average 625 kg ha −1 pa from 1975 to 1979, then 375 kg ha −1 pa subsequently. No fertiliser was applied to one LF (LFNF) and one HF (HFNF) farmlet after 1980. Soil Olsen phosphorus (P) level declined slightly for the LF treatment as maintenance requirements exceeded P inputs; rose in response to increased fertiliser inputs in the HF treatment but more slowly than anticipated maybe because of the poor fertiliser application history and low starting Olsen P; and declined in the HFNF treatment after cessation of fertiliser inputs. Pasture production was 19% greater in HF than LF in year 1, and on average 39% greater in years 2 to 13. Size of earthworm populations measured up to 30 years after treatments commenced was highly related to level of pasture production, and there was no evidence of deleterious effects of superphosphate application on earthworm populations. Pasture production declined by 3% pa in HFNF after fertiliser application ceased. Variability of annual pasture production was large, year-to-year variation about the mean being 35% for HF and 47% for LF (CV = 8.5% and 12.7%, respectively). This suggests comprehensive farm system redesign will not be required in the near or medium term to cope with the gradual onset of climate change, as farmers already manage large year-to-year variability with existing systems and their careful implementation should suffice. Estimated maximum annual production with non-limiting P supply for lowslope (1-12°), medium-slope (13-25°) and steep (>25°) sites was estimated to be 14.8, 11.5 and 9.4 t DM ha −1 from curves derived using Bayesian statistics. The Olsen P level for 97% maximum pasture production was 18 µg g −1 ; converted to a volumetric basis this equates to about 15 µg mL −1 which is lower than the commonly accepted industry guideline for near maximum pasture production of 20 µg mL −1 .

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Developing variable rate application technology: Economic impact for farm owners and topdressing operators

Cited by 15 publications

References 8 publications

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

Integrating Airborne Hyperspectral, Topographic, and Soil Data for Estimating Pasture Quality Using Recursive Feature Elimination with Random Forest Regression

The role of precision agriculture for improved nutrient management on farms

Responses of grazed New Zealand hill pastures to rates of superphosphate application

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