ORCID Profile
0000-0002-7329-1289
Current Organisation
Murdoch University
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Publisher: MDPI AG
Date: 04-06-2021
DOI: 10.3390/RS13112202
Abstract: Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD) (2) curve fitting that incorporates sowing date information (+SD) and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha) (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.
Publisher: Wiley
Date: 28-01-2013
DOI: 10.1002/PS.3459
Abstract: The eradicability of rain-splashed crop diseases was examined by modelling the spread of lupin anthracnose over a spatially heterogeneous landscape. Two hypotheses were investigated: (i) in most cases, rain-splashed diseases are unlikely to be eradicable because spread will be too extensive by the time the disease is detected (ii) there are recognisable characteristics of an incursion that can be used to identify cases when the disease will be eradicable. Results indicate that the eradication of a rain-splashed crop disease is heavily dependent on the surveillance effort, on how detectable the disease is and on whether there are susceptible hosts outside the cropping area. These simple indicators can be used to estimate the potential for success of an eradication scheme. Eradication targeting only the crop area is destined to fail, unless it is certain that no susceptible host lies adjacent to the cropping area. A failed eradication attempt can be costly, and a simple set of indicators for the likelihood of success is extremely useful. These indicators can aid decision-makers when faced with a new incursion, identifying when there is little hope of success. © 2012 Society of Chemical Industry.
Publisher: Elsevier BV
Date: 07-2019
Publisher: Inter-Research Science Center
Date: 21-01-2021
DOI: 10.3354/MEPS13540
Abstract: Penaeid prawns (shrimp) are short-lived and fecund, with a complicated life cycle that includes offshore spawning followed by a coastal or estuarine postlarval and juvenile phase. Factors affecting survival during the early life-history stages, and during movement between these stages, will affect variability in recruitment to the nursery ground, the offshore subadult and adult population, and, ultimately, catch. The inability to predict recruitment, and ultimately commercial offshore catch, has been complicated by an incomplete understanding of these factors. The reproductive dynamics of Penaeus ( Fenneropenaeus ) merguiensis were investigated by simultaneous adult and larval s ling on 66 lunar-monthly surveys from March 1986 to March 1992 in Albatross Bay, northeastern Gulf of Carpentaria, Australia. Egg production was seasonal, with the highest production from 6-mo-old newly recruited spawners, and another peak from 12mo-old spawners. Larval abundance (no. m -2 ) followed the same seasonal pattern as the abundance of eggs. However, interannual variation in egg and larval abundance was large, and there was a weak correlation between monthly egg and larval abundance. Larval abundance appeared to be further influenced by fluctuations in chlorophyll a concentration, a measure of food availability. There was evidence of a match/mismatch relationship between larval abundance and episodic chlorophyll increases. While there was no direct spawner (egg production)-fishery recruit relationship in P. merguiensis over the 6-yr study, there was a strong relationship between total larval abundance in spring and the size of the commercial catch 3 to 6 mo later. Therefore, factors affecting larval survival, including food availability, have significant implications for fishery production.
Publisher: Elsevier BV
Date: 03-2020
Publisher: Informa UK Limited
Date: 2001
Publisher: Elsevier BV
Date: 08-2010
Publisher: SPIE
Date: 23-01-2001
DOI: 10.1117/12.413948
Publisher: Springer US
Date: 2000
Publisher: MDPI AG
Date: 05-11-2020
Abstract: On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.
Publisher: Springer New York
Date: 2003
Publisher: MDPI AG
Date: 22-06-2021
DOI: 10.3390/RS13132435
Abstract: Satellite remote sensing offers a cost-effective means of generating long-term hindcasts of yield that can be used to understand how yield varies in time and space. This study investigated the use of remotely sensed phenology, climate data and machine learning for estimating yield at a resolution suitable for optimising crop management in fields. We used spatially weighted growth curve estimation to identify the timing of phenological events from sequences of Landsat NDVI and derive phenological and seasonal climate metrics. Using data from a 17,000 ha study area, we investigated the relationships between the metrics and yield over 17 years from 2003 to 2019. We compared six statistical and machine learning models for estimating yield: multiple linear regression, mixed effects models, generalised additive models, random forests, support vector regression using radial basis functions and deep learning neural networks. We used a 50-50 train-test split on paddock-years where 50% of paddock-year combinations were randomly selected and used to train each model and the remaining 50% of paddock-years were used to assess the model accuracy. Using only phenological metrics, accuracy was highest using a linear mixed model with a random effect that allowed the relationship between integrated NDVI and yield to vary by year (R2 = 0.67, MAE = 0.25 t ha−1, RMSE = 0.33 t ha−1, NRMSE = 0.25). We quantified the improvements in accuracy when seasonal climate metrics were also used as predictors. We identified two optimal models using the combined phenological and seasonal climate metrics: support vector regression and deep learning models (R2 = 0.68, MAE = 0.25 t ha−1, RMSE = 0.32 t ha−1, NRMSE = 0.25). While the linear mixed model using only phenological metrics performed similarly to the nonlinear models that are also seasonal climate metrics, the nonlinear models can be more easily generalised to estimate yield in years for which training data are unavailable. We conclude that long-term hindcasts of wheat yield in fields, at 30 m spatial resolution, can be produced using remotely sensed phenology from Landsat NDVI, climate data and machine learning.
Publisher: CSIRO Publishing
Date: 07-06-2023
DOI: 10.1071/ES22031
Abstract: Seasonal forecasts are increasingly important tools in agricultural crop management. Regions with Mediterranean-type climates typically adopt rain-fed agriculture with minimal irrigation, hence accurate seasonal forecasts of rainfall during the growing season are potentially useful in decision making. In this paper we examined the bias and skill of a seasonal forecast system (ACCESS-S1) in simulating growing season precipitation (GSP) for south-west Western Australian (SWWA), a region with a Mediterranean-type climate and significant cereal crop production. Focusing on July–September (3-month) and May–October (6-month) forecasts, with 0- and 1-month lead times, we showed that overall ACCESS-S1 had a dry bias for SWWA rainfall and a tendency to simulate close to average rainfall during both wetter and drier than average rainfall years. ACCESS-S1 showed particularly poor skill at these timeframes for very wet and very dry years. The limitations in ACCESS-S1 for SWWA GSP were associated with inaccuracies in the timing of heavy rainfall events. In addition, limitations of the ACCESS-S1 model in accurately capturing SST and wind anomaly patterns over the tropical Indian Ocean during extreme rainfall years also contributed to errors in SWWA GSP forecasts. Model improvements in these regions have the potential to improve seasonal rainfall forecasts for SWWA.
Publisher: Informa UK Limited
Date: 12-2000
Publisher: MDPI AG
Date: 28-05-2021
DOI: 10.3390/RS13112128
Abstract: Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.
Publisher: Elsevier BV
Date: 09-2020
No related grants have been discovered for Fiona Evans.