The nutritional quality of cereals varies geospatially in Ethiopia and Malawi.

Micronutrient deficiencies (MNDs) remain widespread among people in sub-Saharan Africa1-5, where access to sufficient food from plant and animal sources that is rich in micronutrients (vitamins and minerals) is limited due to socioeconomic and geographical reasons4-6. Here we report the micronutrient composition (calcium, iron, selenium and zinc) of staple cereal grains for most of the cereal production areas in Ethiopia and Malawi. We show that there is geospatial variation in the composition of micronutrients that is nutritionally important at subnational scales. Soil and environmental covariates of grain micronutrient concentrations included soil pH, soil organic matter, temperature, rainfall and topography, which were specific to micronutrient and crop type. For rural households consuming locally sourced food-including many smallholder farming communities-the location of residence can be the largest influencing factor in determining the dietary intake of micronutrients from cereals. Positive relationships between the concentration of selenium in grain and biomarkers of selenium dietary status occur in both countries. Surveillance of MNDs on the basis of biomarkers of status and dietary intakes from national- and regional-scale food-composition data1-7 could be improved using subnational data on the composition of grain micronutrients. Beyond dietary diversification, interventions to alleviate MNDs, such as food fortification8,9 and biofortification to increase the micronutrient concentrations in crops10,11, should account for geographical effects that can be larger in magnitude than intervention outcomes.

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.

The 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.

A loss function to evaluate agricultural decision-making under uncertainty: a case study of soil spectroscopy.

Modern sensor technologies can provide detailed information about soil variation which allows for more precise application of fertiliser to minimise environmental harm imposed by agriculture. However, growers should lose neither income nor yield from associated uncertainties of predicted nutrient concentrations and thus one must acknowledge and account for uncertainties. A framework is presented that accounts for the uncertainty and determines the cost-benefit of data on available phosphorus (P) and potassium (K) in the soil determined from sensors. For four fields, the uncertainty associated with variation in soil P and K predicted from sensors was determined. Using published fertiliser dose-yield response curves for a horticultural crop the effect of estimation errors from sensor data on expected financial losses was quantified. The expected losses from optimal precise application were compared with the losses expected from uniform fertiliser application (equivalent to little or no knowledge on soil variation). The asymmetry of the loss function meant that underestimation of P and K generally led to greater losses than the losses from overestimation. This study shows that substantial financial gains can be obtained from sensor-based precise application of P and K fertiliser, with savings of up to £121 ha-1 for P and up to £81 ha-1 for K, with concurrent environmental benefits due to a reduction of 4-17 kg ha-1 applied P fertiliser when compared with uniform application. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-022-09887-2.

Predicting the growth of lettuce from soil infrared reflectance spectra: the potential for crop management.

How well could one predict the growth of a leafy crop from reflectance spectra from the soil and how might a grower manage the crop in the light of those predictions? Topsoil from two fields was sampled and analysed for various nutrients, particle-size distribution and organic carbon concentration. Crop measurements (lettuce diameter) were derived from aerial-imagery. Reflectance 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 differences 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 effects of variety were considered. Predictions from the reflectance 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 fields to maximize the net profit from the crop.

Modelling the effect of spatially variable soil properties on the distribution of weeds.

The patch spraying of weeds is an area of precision agriculture that has had limited uptake. This is in part due to the perceived risks associated with not controlling individual weeds. Nevertheless, the inherent patchiness of weeds makes them ideal targets for site-specific management. We propose using a mechanistic model to identify areas of a field vulnerable to invasion by weeds, allowing the creation of treatment maps that are risk averse. We developed a spatially-explicit mechanistic model of the life-cycle of Alopecurus myosuroides, a particularly problematic weed of cereal crops in the UK. In the model, soil conditions which vary across the field, affect the life-cycle of A. myosuroides. The model was validated using data on the within-field distribution of A. myosuroides on commercial farms and its co-location with soil properties. We demonstrate the important role played by soil properties in determining the within-field distribution of A. myosuroides. We also show that scale-dependent correlations between A. myosuroides and soil properties observed in the field are an emergent property of the modelled dynamics of the A. myosuroides life-cycle. Our model could therefore support effective site-specific management of A. myosuroides within fields by predicting areas that are vulnerable to A. myosuroides. The usefulness of this model in its ability to predict patch locations for A. myosuroides highlights the possibility of using similar models for other species where data are available on the response of the species to various soil properties.

Trade-offs constrain the success of glyphosate-free farming.

Glyphosate, the most widely used herbicide, is linked with environmental harm and there is a drive to replace it in agricultural systems. We model the impacts of discontinuing glyphosate use and replacing it with cultural control methods. We simulate winter wheat arable systems reliant on glyphosate and typical in northwest Europe. Removing glyphosate was projected to increase weed abundance, herbicide risk to the environment, and arable plant diversity and decrease food production. Weed communities with evolved resistance to non-glyphosate herbicides were not projected to be disproportionately affected by removing glyphosate, despite the lack of alternative herbicidal control options. Crop rotations with more spring cereals or grass leys for weed control increased arable plant diversity. Stale seedbed techniques such as delayed drilling and choosing ploughing instead of minimum tillage had varying effects on weed abundance, food production, and profitability. Ploughing was the most effective alternative to glyphosate for long-term weed control while maintaining production and profit. Our findings emphasize the need for careful consideration of trade-offs arising in scenarios where glyphosate is removed. Integrated Weed Management (IWM) with more use of cultural control methods offers the potential to reduce chemical use but is sensitive to seasonal variability and can incur negative environmental and economic impacts.

Wavelet analysis of the variability of nitrous oxide emissions from soil at decameter to kilometer scales.

We analyzed data on nitrous oxide emissions and on soil properties that were collected on a 7.5-km transect across an agricultural landscape in eastern England using the discrete wavelet packet transform. We identified a wavelet packet "best basis" for the emission data. Wavelet packet basis functions are used to decompose the data into a set of coefficients that represent the variation in the data at different spatial frequencies and locations. The "best basis" for a set of data is adapted to the variability in the data by ensuring that the spatial resolution of local features is good at those spatial frequencies where variation is particularly intermittent. The best basis was shown to be adapted to represent such intermittent variation, most markedly at wavelengths of 100 m or less. Variation at these wavelengths was shown to be correlated particularly with chemical properties of the soil, such as nitrate content. Variation at larger wavelengths showed less evidence of intermittency and was found to be correlated with soil chemical and physical constraints on emission rates. In addition to frequency-dependent intermittent variation, it was found that the variance of emission rates at some wavelengths changed at particular locations along the transect. One factor causing this appeared to be contrasts in parent material. The complex variation in emission rates identified by these analyses has implications for how emission rates are estimated.

Soil and landscape factors influence geospatial variation in maize grain zinc concentration in Malawi.

Dietary zinc (Zn) deficiency is widespread globally, and in particular among people in sub-Saharan Africa (SSA). In Malawi, dietary sources of Zn are dominated by maize and spatially dependent variation in grain Zn concentration, which will affect dietary Zn intake, has been reported at distances of up to ~ 100 km. The aim of this study was to identify potential soil properties and environmental covariates which might explain this longer-range spatial variation in maize grain Zn concentration. Data for maize grain Zn concentrations, soil properties, and environmental covariates were obtained from a spatially representative survey in Malawi (n = 1600 locations). Labile and non-labile soil Zn forms were determined using isotopic dilution methods, alongside conventional agronomic soil analyses. Soil properties and environmental covariates as potential predictors of the concentration of Zn in maize grain were tested using a priori expert rankings and false discovery rate (FDR) controls within the linear mixed model (LMM) framework that informed the original survey design. Mean and median grain Zn concentrations were 21.8 and 21.5 mg kg-1, respectively (standard deviation 4.5; range 10.0-48.1). A LMM for grain Zn concentration was constructed for which the independent variables: soil pH(water), isotopically exchangeable Zn (ZnE), and diethylenetriaminepentaacetic acid (DTPA) extractable Zn (ZnDTPA) had predictive value (p < 0.01 in all cases, with FDR controlled at < 0.05). Downscaled mean annual temperature also explained a proportion of the spatial variation in grain Zn concentration. Evidence for spatially dependent variation in maize grain Zn concentrations in Malawi is robust within the LMM framework used in this study, at distances of up to ~ 100 km. Spatial predictions from this LMM provide a basis for further investigation of variations in the contribution of staple foods to Zn nutrition, and where interventions to increase dietary Zn intake (e.g. biofortification) might be most effective. Other soil and landscape factors influencing spatially dependent variation in maize grain Zn concentration, along with factors operating over shorter distances such as choice of crop variety and agronomic practices, require further exploration beyond the scope of the design of this survey.

Spatial prediction of the concentration of selenium (Se) in grain across part of Amhara Region, Ethiopia.

Grain and soil were sampled across a large part of Amhara, Ethiopia in a study motivated by prior evidence of selenium (Se) deficiency in the Region's population. The grain samples (teff, Eragrostis tef, and wheat, Triticum aestivum) were analysed for concentration of Se and the soils were analysed for various properties, including Se concentration measured in different extractants. Predictive models for concentration of Se in the respective grains were developed, and the predicted values, along with observed concentrations in the two grains were represented by a multivariate linear mixed model in which selected covariates, derived from remote sensor observations and a digital elevation model, were included as fixed effects. In all modelling steps the selection of predictors was done using false discovery rate control, to avoid over-fitting, and using an α-investment procedure to maximize the statistical power to detect significant relationships by ordering the tests in a sequence based on scientific understanding of the underlying processes likely to control Se concentration in grain. Cross-validation indicated that uncertainties in the empirical best linear unbiased predictions of the Se concentration in both grains were well-characterized by the prediction error variances obtained from the model. The predictions were displayed as maps, and their uncertainty was characterized by computing the probability that the true concentration of Se in grain would be such that a standard serving would not provide the recommended daily allowance of Se. The spatial variation of grain Se was substantial, concentrations in wheat and teff differed but showed the same broad spatial pattern. Such information could be used to target effective interventions to address Se deficiency, and the general procedure used for mapping could be applied to other micronutrients and crops in similar settings.

Defining the habitat niche of Alopecurus myosuroides at the field scale.

The distribution of Alopecurus myosuroides (black-grass) in fields is patchy. The locations of these patches can be influenced by the environment. This presents an opportunity for precision management through patch spraying. We surveyed five fields on various types of soil using a nested sampling design and recorded both A. myosuroides seedlings in autumn and seed heads in summer. We also measured soil properties at those sampling locations. We found that the patches of seed heads within a field were smaller than the seedling patches, suggesting that techniques for patch spraying based on maps of heads in the previous season could be inherently risky. We also found that the location of A. myosuroides patches within fields can be predicted through their relationship with environmental properties and that these relations are consistent across fields on different soil types. This improved understanding of the relations between soil properties and A. myosuroides seedlings could allow farmers to use pre-existing or suitably supplemented soil maps already in use for the precision application of fertilisers as a starting point in the creation of herbicide application maps.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales.

Weeds tend to aggregate in patches within fields, and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at various scales, the strength of the relations between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We developed a general method that uses novel within-field nested sampling and residual maximum-likelihood (reml) estimation to explore scale-dependent relations between weeds and soil properties. We validated the method using a case study of Alopecurus myosuroides in winter wheat. Using reml, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales, we optimised the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Stability analysis of the FitzHugh-Nagumo differential equations driven by impulses: applied to the electrical firing of magnocellular neurons.

A stability analysis is carried out for a mathematical model which describes the electrical firing of a single vasopressin neuron. The model used in a FitzHugh-Nagumo-type system which is driven by impulses. The analysis is based on recent developments in the stability theory of impulsive differential equations. Conditions are derived under which the system of differential equations is stable at two of its equilibrium points. Biologically this bistability represents the cell alternating between periods of electrical activity and silence. The conditions for stability are specified in terms of the amplitude and frequency of the impulses perturbing the system. Both stochastic and deterministic impulses are considered.

Control analysis of the Rose-Hindmarsh model for neural activity.

It is well known that vasopressin cells fire action potentials in bursts, but also have the ability to continuously discharge or have long periods of silence. Experimentally, various external stimuli can be applied to the vasopressin cell in order to manipulate the patterns of discharge observed. In this paper, the Rose-Hindmarsh model is used to describe the discharging of a single vasopressin cell. The range of parameter values which makes the model display the various patterns observed in vivo are described. It is shown that the model can be controlled to follow desired patterns of discharge and thus mimic external stimulation.

PACE BOM chemotherapy: a 12-week regimen for advanced Hodgkin's disease.

BACKGROUND: This study was designed to evaluate the efficacy and toxicity of a 12-week alternating weekly chemotherapy regimen for advanced Hodgkin's disease. Consolidative irradiation of residual masses was used in selected cases. PATIENTS AND METHODS: Eighty-three patients with newly diagnosed advanced Hodgkin's disease (bulky stage IIA, stage IIB-IVB) or with progressive disease after extended field radiotherapy for early stage disease were included in this study. The patients were treated for 12 weeks with PACE BOM comprising oral prednisolone together with intravenous doxorubicin, cyclophosphamide and etoposide alternating weekly with intravenous bleomycin, vincristine and methotrexate. Limited field adjuvant radiotherapy was also given to 21 patients with localised persistent radiological abnormalities visible on chest X-ray after chemotherapy. The study end points were overall survival, failure free survival (FFS) and toxicity, particularly with respect to reproductive function. RESULTS: With a median post treatment follow up of 52 months the actuarial 5-year overall survival is 90% (confidence interval 81%-95%) and FFS is 64% (52%-74%). This treatment was well tolerated and fertility was maintained in a high proportion of young adults. CONCLUSIONS: The brief duration PACE BOM regimen with or without radiotherapy appears to be comparable in efficacy to other doxorubicin containing regimens, with a favourable toxicity profile. Randomised clinical trials are now needed to evaluate the role of this and comparable initial treatment approaches to advanced Hodgkin's disease.