Browsing by Author "Hassall, Kirsty L."

Now showing 1 - 7 of 7
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    Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time
    (Springer Nature, 2022-03-25) Prout, Jonah M.; Shepherd, Keith D.; McGrath, Steve P.; Kirk, Guy J. D.; Hassall, Kirsty L.; Haefele, Stephan M.
    Realistic targets for soil organic carbon (SOC) concentrations are needed, accounting for differences between soils and land uses. We assess the use of SOC/clay ratio for this purpose by comparing changes over time in (a) the National Soil Inventory of England and Wales, first sampled in 1978–1983 and resampled in 1994–2003, and (b) two long-term experiments under ley-arable rotations on contrasting soils in the East of England. The results showed that normalising for clay concentration provides a more meaningful separation between land uses than changes in SOC alone. Almost half of arable soils in the NSI had degraded SOC/clay ratios (< 1/13), compared with just 5% of permanent grass and woodland soils. Soils with initially large SOC/clay ratios (≥ 1/8) were prone to greater losses of SOC between the two NSI samplings than those with smaller ratios. The results suggest realistic long-term targets for SOC/clay in arable, ley grass, permanent grass and woodland soils are 1/13, 1/10, and > 1/8, respectively. Given the wide range of soils and land uses across England and Wales in the datasets used to test these targets, they should apply across similar temperate regions globally, and at national to sub-regional scales.
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    Evidence of collaborative opportunities to ensure long-term sustainability in African farming
    (Elsevier, 2023-03-15) El Fartassi, Imane; Milne, Alice E.; El Alami, Rafiq; Rafiqi, Maryam; Hassall, Kirsty L.; Waine, Toby W.; Zawadzka, Joanna Ewa; Diarra, Alhousseine; Corstanje, Ron
    Farmers 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.
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    Facilitating the elicitation of beliefs for use in Bayesian Belief modelling
    (Elsevier, 2019-12-01) Hassall, Kirsty L.; Dailey, Gordon; Zawadzka, Joanna Ewa; 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)
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    Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study
    (Elsevier, 2017-12-11) Whetton, Rebecca L.; Hassall, Kirsty L.; Waine, Toby W.; Mouazen, Abdul M.
    This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions.
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    A Moroccan soil spectral library use framework for improving soil property prediction: evaluating a geostatistical approach
    (Elsevier, 2024-12-01) Asrat, Tadesse Gashaw; Breure, Timo; Sakrabani, Ruben; Corstanje, Ron; Hassall, Kirsty L.; Hamma, Abdellah; Kebede, Fassil; Haefele, Stephan M.
    A soil spectrum generated by any spectrometer requires a calibration model to estimate soil properties from it. To achieve best results, the assumption is that locally calibrated models offer more accurate predictions. However, achieving this higher accuracy comes with associated costs, complexity, and resource requirements, thus limiting widespread adoption. Furthermore, there is a lack of comprehensive frameworks for developing and utilizing soil spectral libraries (SSLs) to make predictions for specific samples. While calibration samples are necessary, there is the need to optimize SSL development through strategically determining the quantity, location, and timing of these samples based on the quality of the information in the library. This research aimed to develop a spatially optimized SSL and propose a use-framework tailored for predicting soil properties for a specific farmland context. Consequently, the Moroccan SSL (MSSL) was established utilizing a stratified spatially balanced sampling design, using six environmental covariates and FAO soil units. Subsequently, various criteria for calibration sample selection were explored, including a spatial autocorrelation of spectra principal component (PC) scores (spatial calibration sample selection), spectra similarity memory-based learner (MBL), and selection based on environmental covariate clustering. Twelve soil properties were used to evaluate these calibration sample selections to predict soil properties using the near infrared (NIR) and mid infrared (MIR) ranges. Among the methods assessed, we observed distinct precision improvements resulting from spatial sample selection and MBL compared to the use of the entire MSSL. Notably, the Lin's Concordance Correlation Coefficient (CCC) values using the spatial calibration sample selection was improved for Olsen extractable phosphorus (OlsenP) by 41.3% and Mehlich III extractable phosphorus (P_M3) by 8.5% for the MIR spectra and for CEC by 25.6%, pH by 13.0% and total nitrogen (Tot_N) by 10.6% for the NIR spectra in reference to use of the entire MSSL. Utilizing the spatial autocorrelation of the spectra PC scores proved beneficial in identifying appropriate calibration samples for a new sample location, thereby enhancing prediction performance comparable to, or surpassing that of the use of the entire MSSL. This study signifies notable advancement in crafting targeted models tailored for specific samples within a vast and diverse SSL.
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    Putting numbers to a metaphor: a Bayesian Belief Network with which to infer soil quality and health
    (Elsevier, 2025-07) Hassall, Kirsty L.; Zawadzka, Joanna Ewa; Milne, Alice E.; Corstanje, Ronald; Harris, Jim A.; Dailey, A Gordon; Keith, Aidan M.; Glendining, Margaret J.; McGrath, Stephen P.; Todman, Lindsay C.; Alexander, Paul; Arnold, Philippa; Bennett, Amanda J.; Bhogal, Anne; Clark, Joanna M.; Crotty, Felicity V.; Horrocks, Claire; Noble, Nicola; Rees, Robert; Shepherd, Matthew; Stockdale, Elizabeth A.; Tipping, Edward W.; Whitmore, Andrew P.
    Soil Quality or Soil Health are terms adopted by the scientific community as metaphors for the effects of differing land management practices on the properties and functions of soil. Because they are metaphors, consistent quantitative definitions are lacking. We present here an approach based on expert elicitation in the field of soil function and management that offers a universal way of putting numbers to the metaphor. Like humans, soils differ and so do the ways in which they are understood to become unhealthy. Long-term experiments such as the Broadbalk Wheat experiment at Rothamsted provide unparalled sources of data with which to investigate the state and changes of soil quality and health that have developed from known management over timescales of one hundred years or more. Similarly, large-scale datasets such as the National Soils Inventory and Countryside Survey provide rich resources to explore the geographical variability of soil quality and health in different places against a background of different observed management practices. We structure experts’ views of the extent to which soil delivers the functions expected of it within Bayesian Belief Networks anchored by measurable properties of soil. With these networks, we infer the likely state of soil (i) on Broadbalk, (ii) at locations throughout England & Wales as well as inferring (iii) the most straightforward ways of improving soil quality and health at the locations in (ii). Our methodology has general applicability and could be deployed elsewhere or in other disciplines.
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    Spectral soil analysis for fertilizer recommendations by coupling with QUEFTS for maize in East Africa: A sensitivity analysis
    (Elsevier, 2023-02-24) Asrat, Tadesse Gashaw; Sakrabani, Ruben; Corstanje, Ronald; Breure, Timo; Hassall, Kirsty L.; Kebede, Fassil; Haefele, Stephan M.
    Laboratory analysis of soil properties is prohibitively expensive and difficult to scale across the soils in sub-Saharan Africa. This results in a lack of soil-specific fertilizer recommendations, where recommendation can only be provided at a regional scale. This study aims to assess the feasibility of using spectral soil analysis to provide soil-specific fertilizer recommendations. Using a range of spectrometers [NeoSpectra Saucer (NIR), FieldSpec 4 (vis-NIR) with contact probe or mug light interface, FTIR Bruker Tensor 27 (MIR)], 346 archived soil samples (0–20 cm) with known soil chemical properties collected from Ethiopia, Kenya and Tanzania were scanned. Partial least square regression (PLSR) was used to develop prediction models for selected soil properties including pH, soil organic carbon (SOC), total nitrogen, Olsen P, and exchangeable K. These predicted properties, and associated uncertainty, were used to derive fertilizer recommendations for maize using the Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model parameters for sub-Saharan Africa. Most soil properties (pH, SOC, total nitrogen, and exchangeable K) were well predicted (Concordance Correlation Coefficient values between 0.88 and 0.96 and Ratio of Performance to Interquartile values between 1.4 and 5.9) by all the spectrometers but there were performance variations between soil properties and spectrometers. Use of the predicted soil data for the development of fertilizer recommendations gave promising results when compared to the recommendations obtained with the conventional soil analysis. For example, the least performing NeoSpectra Saucer over/under-estimated up to 8 and 24 kg ha-1N and P, respectively, though there was insignificant variation in estimation of P fertilizer among spectrometers. We conclude that spectral technology can be used to determine major soil properties with satisfactory precision, sufficient for specific fertilizer decision making in East Africa, possibly even with portable equipment in the field.