Browsing by Author "Milne, Alice E."
Now showing 1 - 8 of 8
Results Per Page
Sort Options
Item Open Access Agricultural decision-making under uncertainty: a loss function on the kriging variance from soil properties predicted by infrared and X-ray fluorescence spectroscopy(EGU: European Geophysical Union, 2021-04-30) Breure, Timo Samuel; Haefele, Stephan M.; Webster, Richard; Hannam, Jacqueline A.; Corstanje, Ronald; Milne, Alice E.Item Open Access Comparing the effect of different sample conditions and spectral libraries on the prediction accuracy of soil properties from near- and mid-infrared spectra at the field-scale(Elsevier, 2021-10-07) Breure, Timo Samuel; Prout, Jonah M.; Haefele, Stephan M.; Milne, Alice E.; Hannam, Jacqueline A.; Moreno-Rojas, S.; Corstanje, RonaldThe prediction accuracy of soil properties by proximal soil sensing has made their application more practical. However, in order to gain sufficient accuracy, samples are typically air-dried and milled before spectral measurements are made. Calibration of the spectra is usually achieved by making wet chemistry measurements on a subset of the field samples and local regression models fitted to aid subsequent prediction. Both sample handling and wet chemistry can be labour and resource intensive. This study aims to quantify the uncertainty associated with soil property estimates from different methods to reduce effort of field-scale calibrations of soil spectra. We consider two approaches to reduce these expenses for predictions made from visible-near-infrared ((V)NIR), mid-infrared (MIR) spectra and their combination. First, we considered reducing the level of processing of the samples by comparing the effect of different sample conditions (in-situ, unprocessed, air-dried and milled). Second, we explored the use of existing spectral libraries to inform calibrations (based on milled samples from the UK National Soil Inventory) with and without ‘spiking’ the spectral libraries with a small subset of samples from the study fields. Prediction accuracy of soil organic carbon, pH, clay, available P and K for each of these approaches was evaluated on samples from agricultural fields in the UK. Available P and K could only be moderately predicted with the field-scale dataset where samples were milled. Therefore this study found no evidence to suggest that there is scope to reduce costs associated with sample processing or field-scale calibration for available P and K. However, the results showed that there is potential to reduce time and cost implications of using (V)NIR and MIR spectra to predict soil organic carbon, clay and pH. Compared to field-scale calibrations from milled samples, we found that reduced sample processing lowered the ratio of performance to inter-quartile range (RPIQ) between 0% and 76%. The use of spectral libraries reduced the RPIQ of predictions relative to field-scale calibrations from milled samples between 54% and 82% and the RPIQ was reduced between 29% and 70% for predictions when spectral libraries were spiked. The increase in uncertainty was specific to the combination of soil property and sensor analysed. We conclude that there is always a trade-off between prediction accuracy and the costs associated with soil sampling, sample processing and wet chemical analysis. Therefore the relative merits of each approach will depend on the specific case in question.Item Open Access Evidence of collaborative opportunities to ensure long-term sustainability in African farming(Elsevier, 2023-02-17) El Fartassi, Imane; Milne, Alice E.; El Alami, Rafiq; Rafiqi, Maryam; Hassall, Kirsty L.; Waine, Toby W.; Zawadzka, Joanna; Diarra, Alhousseine; Corstanje, RonFarmers face the challenge of increasing production to feed a growing population and support livelihoods, whilst also improving the sustainability and resilience of cropping systems. Understanding the key factors that influence farming management practices is crucial for determining farmers' adaptive capacity and willingness to engage in cooperative strategies. To that end, we investigated management practices that farmers adopt and the factors underlying farmers' decision-making. We also aimed to identify the constraints that impede the adoption of strategies perceived to increase farming resilience and to explore how the acceleration of technology adoption through cooperation could ensure the long-term sustainability of farming. Surveys were distributed to farming stakeholders and professionals who worked across the contrasting environments of Morocco. We used descriptive statistics and analysis by log-linear modelling to predict the importance of factors influencing farmers’ decision-making. The results show that influencing factors tended to cluster around environmental pressures, crop characteristics and water availability with social drivers playing a lesser role. Subsidies were also found to be an important factor in decision-making. Farming stakeholders generally believed that collaborative networks are likely to facilitate the adoption of sustainable agricultural practices. We conclude that farmers need both economic incentives and technical support to enhance their adaptive capacity as this can lessen the socioeconomic vulnerability inherent in arid and semi-arid regions.Item Open Access Facilitating the elicitation of beliefs for use in Bayesian Belief modelling(Elsevier, 2019-10-01) Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna; Milne, Alice E.; Harris, Jim A.; Corstanje, Ronald; Whitmore, Andrew P.Expert opinion is increasingly being used to inform Bayesian Belief Networks, in particular to define the conditional dependencies modelled by the graphical structure. The elicitation of such expert opinion remains a major challenge due to both the quantity of information required and the ability of experts to quantify subjective beliefs effectively. In this work, we introduce a method designed to initialise conditional probability tables based on a small number of simple questions that capture the overall shape of a conditional probability distribution before enabling the expert to refine their results in an efficient way. These methods have been incorporated into a software Application for Conditional probability Elicitation (ACE), freely available at https://github.com/KirstyLHassall/ACE Hassall (2019)Item Open Access A model of the effect of fungicides on disease-induced yield loss, for use in wheat disease management decision support systems(Blackwell Publishing Ltd., 2007-08-15T00:00:00Z) Milne, Alice E.; Paveley, Neil; Audsley, Eric; Parsons, David J.A model of the effect of foliar-applied fungicides on disease-induced yield loss is described, parameterised and tested. The effects of fungicides on epidemics of Septoria tritici (leaf blotch), Puccinia striiformis (yellow rust), Blumeria graminis f.sp. tritici (powdery mildew) and Puccinia triticina (brown rust) on winter wheat were simulated using dose-response curve parameters. Where two or more active substances were applied together, their joint action was estimated using an additive dose model where the active substances had the same mode of action or a multiplicative survival model where the modes of action differed. By coupling the model with models of wheat canopy growth and foliar disease published previously, it was possible to estimate disease-induced yield loss for a prescribed fungicide programme. The difference in green canopy area and, hence, interception of photosynthetically active radiation between simulated undiseased and diseased (but treated) crop canopies was used to estimate yield loss. The model was tested against data front field experiments across a range of sites, seasons and wheat cultivars and was shown to predict the observed disease-induced yield loss with sufficient accuracy to support fungicide treatment decisions. A simple method Of accounting for uncertainty in the predictions of yield loss is described. Fungicide product, dose and spray timing combinations selected using the coupled models responded appropriately to disease pressure and cultivar disease resistance.Item Open Access Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management(Springer, 2020-08-10) Breure, Timo Samuel; Milne, Alice E.; Webster, R.; Haefele, Stephan M.; Hannam, Jacqueline A.; Moreno-Rojas, S.; Corstanje, RonaldHow well could one predict the growth of a leafy crop from refectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two felds was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Refectance spectra were obtained in the laboratory from the soil in the near- and mid-infrared ranges, and these were used to predict crop performance by partial least squares regression (PLSR). Individual soil properties were also predicted from the spectra by PLSR. These estimated soil properties were used to predict lettuce diameter with a linear model (LM) and a linear mixed model (LMM): considering diferences between lettuce varieties and the spatial correlation between data points. The PLSR predictions of the soil properties and lettuce diameter were close to observed values. Prediction of lettuce diameter from the estimated soil properties with the LMs gave somewhat poorer results than PLSR that used the soil spectra as predictor variables. Predictions from LMMs were more precise than those from the PLSR using soil spectra. All model predictions improved when the efects of variety were considered. Predictions from the refectance spectra, via the estimation of soil properties, can enable growers to decide what treatments to apply to grow lettuce and how to vary their treatments within their felds to maximize the net proft from the cropItem Open Access Using stochastic dynamic programming to support weed management decisions over a rotation(Blackwell Publishing Ltd, 2009-04-01T00:00:00Z) Benjamin, L. R.; Milne, Alice E.; Lutman, P. J. W.; Parsons, David J.; Cussans, J.; Storkey, J.This study describes a model that predicts the impact of weed management on the population dynamics of arable weeds over a rotation and presents the economic consequences. A stochastic dynamic programming optimisation is applied to the model to identify the management strategy that maximises gross margin over the rotation. The model and dynamic programme were developed for the weed management decision support system -'Weed Manager'. Users can investigate the effect of management practices (crop, sowing time, weed control and cultivation practices) on their most important weeds over the rotation or use the dynamic programme to evaluate the best theoretical weed management strategy. Examples of the output are given in this paper, along with discussion on their validation. Through this study, we demonstrate how biological models can (i) be integrated into a decision framework and (ii) deliver valuable weed management guidance to users.Item Open Access Weed Manager-A model-based decision support system for weed management in arable crops(Elsevier Science B.V., Amsterdam., 2009-03-01T00:00:00Z) Parsons, David J.; Benjamin, L. R.; Clarke, J.; Ginsburg, D.; Mayes, A.; Milne, Alice E.; Wilkinson, D. J.Weed Manager is a model-based decision support system to assist arable farmers and advisers in weed control decisions on two time scales: within a single season and over several years in a rotation. The single season decision is supported by a wheat crop and annual weed growth simulation, with a multi-stage heuristic decision model. The rotational aspect uses a model of seed population dynamics, with decisions optimised using stochastic dynamic programming. Each time scale has its own user interface within a single program integrated into the ArableDS framework, which provides data sharing between several decision support modules. Weed Manager was used by about 100 farmers and consultants in the 2005–2006 and 2006–2007 seas