Browsing by Author "Taylor, John C."
Now showing 1 - 11 of 11
Results Per Page
Sort Options
Item Open Access The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan(Taylor and Francis, 2014-08-26) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Juniper, Graham R.We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.Item Open Access Calibration methodology for mapping within-field crop variability using remote sensing(Elsevier Science, 2003-04) Wood, G. A.; Taylor, John C.; Godwin, R. J.A successful method of mapping within-field crop variability of shoot populations in wheat (Triticum aestivum) and barley (Hordeum vulgare L.) is demonstrated. The approach is extended to include a measure of green area index (GAI). These crop parameters and airborne remote sensing measures of the normalised difference vegetation index (NDVI) are shown to be linearly correlated. Measurements were made at key agronomic growth stages up to the period of anthesis and correlated using statistical linear regression based on a series of field calibration sites. Spatial averaging improves the estimation of the regression parameters and is best achieved by sub-sampling at each calibration site using three 0·25 m2 quadrats. Using the NDVI image to target the location of calibration sites, eight sites are shown to be sufficient, but they must be representative of the range in NDVI present in the field, and have a representative spatial distribution. Sampling the NDVI range is achieved by stratifying the NDVI image and then randomly selecting within each of the strata; ensuring a good spatial distribution is determined by visual interpretation of the image. Similarly, a block of adjacent fields can be successfully calibrated to provide multiple maps of within-field variability in each field using only eight points per block representative of the NDVI range and constraining the sampling to one calibration site per field. Compared to using 30 or more calibration sites, restricting samples to eight does not affect the estimation of the regression parameters as long as the criteria for selection outlined in this paper is adhered to. In repeated tests, the technique provided regression results with a value for the coefficient of determination of 0·7 in over 85% of cases. At farm scale, the results indicate an 80–90% probability of producing a map of within crop field variability with an accuracy of 75–99%. This approach provides a rapid tool for providing accurate and valuable management information in near real-time to the grower for better management and for immediate adoption in precision farming practices, and for determining variable rates of nitrogen, fungicide or plant growth regulators.Item Open Access Developing strategies for spatially variable nitrogen application in cereals II: wheat(Elsevier Science, 2003-04) Welsh, J. P.; Wood, G. A.; Godwin, R. J.; Taylor, John C.; Earl, R.; Blackmore, S.; Knight, S. M.For precision agriculture to provide both economic and environmental benefits over conventional farm practice, management strategies must be developed to accommodate the spatial variability in crop performance that occurs within fields. Experiments were established in crops of winter barley (Hordeum vulgare L.) over three seasons. The aim of which was to evaluate a set of variable rate nitrogen strategies and examining the spatial variation in crop response to applied N. The optimum N application rate varied from 90 to in excess of 160 kg [N] ha−1 in different parts of the field, which supports the case for applying spatially variable rates of N. This, however, is highly dependent on seasonal variations, e.g. the quantity and distribution of rainfall and the effect that this has on soil moisture deficits and crop growth. Estimates of yield potential, produced from either historic yield data or shoot density maps derived from airborne digital photographic images, were used to divide experimental strips into management zones. These zones were then managed according to two N application strategies. The results from the historic yield approach, based on 3 yr of yield data, were inconsistent, and it was concluded that that this approach, which is currently the most practical commercial system, does not provide a suitable basis for varying N rates. The shoot density approach, however, offered considerably greater potential as it takes account of variation in the current crop. Using this approach, it was found that applying additional N to areas with a low shoot population and reducing N to areas with a high shoot population resulted in an average strategy benefit of up to 0·36 t ha−1 compared with standard farm practice.Item Open Access Developing Strategies for spatially variable nitrogen application in cereals, I: Winter barley(Elsevier Science, 2003-04) Welsh, J. P.; Wood, G. A.; Godwin, R. J.; Taylor, John C.; Earl, R.; Blackmore, S.; Knight, S. M.For precision agriculture to provide both economic and environmental benefits over conventional farm practice, management strategies must be developed to accommodate the spatial variability in crop performance that occurs within fields. Experiments were established in crops of winter barley (Hordeum vulgare L.) over three seasons. The aim of which was to evaluate a set of variable rate nitrogen strategies and examining the spatial variation in crop response to applied N. The optimum N application rate varied from 90 to in excess of 160 kg [N] ha−1 in different parts of the field, which supports the case for applying spatially variable rates of N. This, however, is highly dependent on seasonal variations, e.g. the quantity and distribution of rainfall and the effect that this has on soil moisture deficits and crop growth. Estimates of yield potential, produced from either historic yield data or shoot density maps derived from airborne digital photographic images, were used to divide experimental strips into management zones. These zones were then managed according to two N application strategies. The results from the historic yield approach, based on 3 yr of yield data, were inconsistent, and it was concluded that that this approach, which is currently the most practical commercial system, does not provide a suitable basis for varying N rates. The shoot density approach, however, offered considerably greater potential as it takes account of variation in the current crop. Using this approach, it was found that applying additional N to areas with a low shoot population and reducing N to areas with a high shoot population resulted in an average strategy benefit of up to 0·36 t ha−1 compared with standard farm practice.Item Open Access Image segmentation for improved consistency in image-interpretation of opium poppy(Taylor and Francis, 2016-02-18) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.; Brewer, Timothy R.The image-interpretation of opium poppy crops from very high resolution satellite imagery forms part of the annual Afghanistan opium surveys conducted by the United Nations Office on Drugs and Crime and the United States Government. We tested the effect of generalization of field delineations on the final estimates of poppy cultivation using survey data from Helmand province in 2009 and an area frame sampling approach. The sample data was reinterpreted from pan-sharpened IKONOS scenes using two increasing levels of generalization consistent with observed practice. Samples were also generated from manual labelling of image segmentation and from a digital object classification. Generalization was found to bias the cultivation estimate between 6.6% and 13.9%, which is greater than the sample error for the highest level. Object classification of image-segmented samples increased the cultivation estimate by 30.2% because of systematic labelling error. Manual labelling of image-segmented samples gave a similar estimate to the original interpretation. The research demonstrates that small changes in poppy interpretation can result in systematic differences in final estimates that are not included within confidence intervals. Segmented parcels were similar to manually digitized fields and could provide increased consistency in field delineation at a reduced cost. The results are significant for Afghanistan’s opium monitoring programmes and other surveys where sample data are collected by remote sensing.Item Open Access Improved estimates of opium cultivation in Afghanistan using imagery-based stratification(Taylor and Francis, 2017-03-30) Simms, Daniel M.; Waine, Toby W.; Taylor, John C.The United Nations O ce on Drugs and Crime and the US Government make extensive use of remote sensing to quantify and monitor trends in Afghanistans illicit opium production. Cultivation gures from their independent annual surveys can vary because of systematic di erences in survey methodologies relating to spectral strati cation and the addition of a pixel bu er to the agricultural area. We investigated the e ect of strati cation and bu ering on area estimates of opium poppy using SPOT5 imagery covering the main opium cultivation area of Helmand province and sample data of poppy elds interpreted from very high resolution satellite imagery. The e ect of resolution was investigated by resampling the original 10 m pixels to 20, 30 and 60 m, representing the range of available imagery. The number of strata (1, 4, 8, 13, 23, 40) and sample fraction (0.2 to 2%) used in the estimate were also investigated. Strati cation reduced the con dence interval by improving the precision of estimates. Cultivation estimates of poppy using 40 spectral strata and a sample fraction of 1.1% had a similar precision to direct expansion estimates using a 2% sample fraction. Strati ed estimates were more robust to changes in sample size and distribution. The mapping of the agricultural area had a signi cant e ect on poppy cultivation estimates in Afghanistan, where the area of total agricultural production can vary signi cantly between years. The ndings of this research explain di erences in cultivation gures of the opium monitoring programmes in Afghanistan and recommendations can be applied to improve resource monitoring in other geographic areas.Item Open Access Real-time measures of canopy size as a basis for spatially varying nitrogen applications to winter wheat sown at different seed rates(Elsevier Science, 2003-04) Wood, G. A.; Welsh, J. P.; Godwin, R. J.; Taylor, John C.; Earl, R.; Knight, S. M.Experiments at two sites growing winter wheat show that in order to manage a wheat canopy more effectively, the use of specific remote sensing techniques both to monitor crop canopy expansion, and to determine variable nitrogen applications at key timings is required. Variations in seed rate were used to achieve a range of initial crop structures, and treatments were compared to standard farm practice. In the first year, the effect of varying seed rate (250, 350 and 450 seeds m−2) on crop structure, yield components and grain yield, was compared to the effects of underlying spatial variation. Plant populations increased up to the highest rate, but shoot and ear populations peaked at 350 seeds m−2. Compensation through an increased number of grains per ear and thousand grain weight resulted in the highest yield and gross margin at the lowest seed rate. In later experiments, the range of seed rates was extended to include 150 seeds m−2, each sown in 24 m wide strips split into 12 m wide halves. One half received a standard nitrogen dose of 200 kg [N] ha−1, the other a variable treatment based on near ‘real-time’ maps of crop growth. Both were split into three applications, targeted at mid-late tillering (early March), growth stages GS30-31 (mid April) and GS33 (mid May). At each timing, calibrated aerial digital photography was used to assess crop growth in terms of shoot population at tillering, and canopy green area index at GS30-31 and GS33. These were compared to current agronomic guidelines. Application rates were then varied below or above the planned amount where growth was above- or below-target, respectively. In the first field, total nitrogen doses in the variable treatments ranged from 188 to 243 kg [N] ha−1, which gave higher yields than the standards at all seed rates in the range 0·36–0·78 t ha−1 and gross margins of £17 to £60 ha−1. In the second field, variable treatments ranged from 135 to 197 kg [N] ha−1 that resulted in lower yields of −0·32 to +0·30 t ha−1. However, in three out of the four seed rates, variable treatments produced higher gross margins than the standard, which ranged from £2 to £20 ha−1. In both fields, the greatest benefits were obtained where the total amount of applied nitrogen was similar to the standard, but was applied variably rather than uniformly along the strips. Simple nitrogen balance calculations have shown that variable application of nitrogen can have an overall effect on reducing the nitrogen surplus by one-third.Item Open Access Soil factors and their influence on within-field crop variability II: spatial analysis and determination of management zones(Elsevier Science, 2003-04) Taylor, John C.; Wood, G. A.; Earl, R.; Godwin, R. J.Spatial variation of crop yields was examined in three trial cereal fields in England from 1994 through 1997. The fields were managed with uniform inputs but there were considerable differences in the spatial patterns and magnitudes of variation between fields and seasons. Up to 50% of the yield variation was within the tramline spacing distance (20â 24 m) and this appeared to relate to crop management practices rather than underlying soil factors. Longer-range variation generally increased up to field scale but was not constant between seasons. Longer-range variation was more apparent in dry years and was attributable to soil variation. Soil series differences coincided with yield differences in dry years when the soil series differences could be expected to create large differences in soilâ water relationships. Soil electrical conductivity, measured by electromagnetic induction (EMI), was investigated as a surrogate for detailed soil coring. Field zones created by EMI also coincided with yield differences and zones were similar to those delineated by soil series with expected differences in soilâ water relationships. The EMI observations were found to be a useful and cost-effective surrogate for representing soil variability in fields likely to create yield variations. Subdivision of fields into management zones using multi-variate K-means cluster analysis of historical yield and EMI observations formed an objective basis for targeting soil samples for nutrient analysis and development of site-specific application strategies. The appropriateness of site-specific management has to be assessed annually because magnitude and pattern of variation changes from season to season.Item Open Access Soil Factors and their Influence on Within-Field Crop Variability, I: Field Observation of Soil Variation(Elsevier Science B.V., Amsterdam, 2003-04-01T00:00:00Z) Earl, R.; Taylor, John C.; Wood, G. A.; Bradley, I.; James, Iain T.; Waine, Toby W.; Welsh, J. P.; Godwin, R. J.; Knight, S. M.A fundamental component of adopting the concept of precision farming in practice is the ability to measure spatial variation in soil factors and assess the influence of this on crop variability in order to apply appropriate management strategies. The aim of this study was to appraise potential methods for measuring spatial variability in soil type, nutrient status and physical properties in practical farming situations. Five fields that are representative of more than 30% of soils used for arable production in England and Wales were selected for use as case studies. Maps of soil type were generated from a conventional hand auger survey on a 100 m grid and the excavation of targeted soil profile pits. These were compared with those refined using a mechanised soil coring device and scans of electromagnetic inductance (EMI) carried out while the soil could reasonably be considered to be at, or near, field capacity moisture content. In addition, soil sampling for nutrient analyses was conducted on a 50 m grid to examine the spatial variation in nutrient status. Conventional methods for sampling soil were found to be appropriate for identifying soil types at specific locations within the field sites, however, they were time- consuming to perform which placed an economic and therefore a practical limitation on the sampling density possible. The resulting data were considered to be too sparse for demarcating soil type boundaries for use in the context of precision farming. The location of soil boundaries were refined by using the mechanised soil corer, however, the limitation of this was found to be the time required to analyse the soil cores produced. Maps of soil variation generated from EMI scans conducted at field capacity appear to reflect the underlying variation in soil type observed in maps generated using the mechanised soil corer. and, therefore, this approach has potential as a cost-effective, data- rich, surrogate for measures of soil variability. Results from analyses of soil samples for measurement of nutrient status indicated that whilst there was considerable variation in macro- and micro-nutrient levels in each field, with the exception of pH, these levels were above commonly accepted agronomic limits. Results did however demonstrate the potential for addressing variation in critical factors such as pH at specific locations, however, there is a need to develop protocols for targeting sampling in order to reduce costs.Item Open Access Survey and monitoring of opium poppy and wheat in Afghanistan: 2003-2009(2010-04-01) Taylor, John C.; Waine, Toby W.; Juniper, Graham R.; Simms, Daniel M.; Brewer, Timothy R.An integrated application of remote-sensing technology was devised and applied in Afghanistan during 2003–2009 providing critical information on cereal and poppy cultivation and poppy eradication. The results influenced UK and international policy and counter-narcotics actions in Afghanistan.Item Open Access Towards improving the accuracy of opium yield estimates with remote sensing(Taylor and Francis, 2014-08-28) Waine, Toby W.; Simms, Daniel M.; Taylor, John C.; Juniper, Graham R.Yearly estimates of illicit opium production are key metrics for assessing the effectiveness of the counter-narcotics policy in Afghanistan. Poor security often prevents access to sample locations and puts pressure on field surveyors, resulting in biased sampling and errors in data recording. Supportive methods using aerial digital photography for improving yield estimates were investigated in the UK in 2004, 2005, and 2010. There were good empirical relationships between normalized difference vegetation index and poppy yield indicators (mature capsule volume and dry capsule yield) for individual fields. The results suggested a good generalized relationship across all sampled fields and years (R2 > 0.70) during the 3–4 week period including poppy flowering. Regression estimates using this relationship with the imagery counteracted bias in the sample estimate of yield, reduced sample error, and enabled the production of detailed maps showing the poppy yield distribution. The application of this approach using very-high-resolution satellite imagery was investigated in the context of the annual opium survey in Afghanistan. Initial results indicated the potential for bias correction of yield estimates using a smaller and targeted collection of ground observations as an alternative to random sampling.