The implications of spatially variable pre-emergence herbicide efficacy for weed management

BACKGROUND The efficacy of pre-emergence herbicides within fields is spatially variable due to soil heterogeneity. We quantified the effect of soil organic matter on the efficacy of two pre-emergence herbicides; flufenacet and pendimethalin, against A. myosuroides and investigated the implications of variation in organic matter for weed management using a crop-weed competition model. RESULTS Soil organic matter played a critical role in determining the level of control achieved. The high organic matter soil had more surviving weeds with higher biomass than the low organic matter soil. In the absence of competition, surviving plants recovered to produce the same amount of seed as if no herbicide were applied. The competition model predicted that weeds surviving pre-emergence herbicides could compensate for sub-lethal effects even when competing with the crop. The ED50 was higher for weed seed production than seedling mortality or biomass. This difference was greatest on high organic matter soil. CONCLUSION These results show that the application rate of herbicides should be adjusted to account for within-field variation in soil organic matter. The results from the modelling emphasised the importance of crop competition in limiting the capacity of weeds surviving pre-emergence herbicides to compensate and replenish the seedbank

EEG potentials associated with artificial grammar learning in the primate brain

AbstractElectroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved processes. We recorded EEG potentials during an auditory AG learning experiment in two Rhesus macaques. The animals were first exposed to sequences of nonsense words generated by the AG. Then surface-based ERPs were recorded in response to sequences that were ‘consistent’ with the AG and ‘violation’ sequences containing illegal transitions. The AG violations strongly modulated an early component, potentially homologous to the Mismatch Negativity (mMMN), a P200 and a late frontal positivity (P500). The macaque P500 is similar in polarity and time of occurrence to a late EEG positivity reported in human AG learning studies but might differ in functional role

Above‐ and below‐ground assessment of carabid community responses to crop type and tillage

Carabid beetles are major predators in agro‐ecosystems. The composition of their communities within crop environments governs the pest control services they provide. An understudied aspect is the distribution of predacious carabid larvae in the soil. We used novel subterranean trapping with standard pitfall trapping, within a multi‐crop rotation experiment, to assess the responses of above‐ and below‐ground carabid communities to management practices. Crop and trap type significantly affected pooled carabid abundance with an interaction of the two, the highest numbers of carabids were caught in subterranean traps in barley under sown with grass. Trap type accounted for the most variance observed in carabid community composition, followed by crop. Tillage responses were only apparent at the species level for three of the eight species modelled. Responses to crop type varied by species. Most species had higher abundance in under‐sown barley, than grass, wheat and barley. Crop differences were greater in the subterranean trap data. For predaceous larvae, standard pitfalls showed lowest abundances in under‐sown barley, yet subterranean traps revealed abundances to be highest in this crop. Comprehensive estimation of ecosystem services should incorporate both above‐ and below‐ground community appraisal, to inform appropriate management

Species matter when considering landscape effects on carabid distributions

Increasing the abundance and diversity of carabid beetles is a common objective of farm habitat management to deliver sustainable pest control. Carabid spatial distributions in relation to crop areas are important to the delivery of this ecosystem service. We used pitfall count data at distances from edge habitats into crop centres, from farm sites across the UK, to determine the effects of in-field and adjacent environmental features on carabid abundance and diversity. Overall carabid abundance increased towards the crop centre, whilst species richness and diversity decreased. The analyses of carabid abundance based on all the species pooled together strongly reflected the behaviour of the most abundant species. Species preferences varied by crop, soil type, and environmental features. For instance, some species were positively associated with habitats such as margins, while others responded negatively. This contrast in individual species models highlights the limitations on pooled models in elucidating responses. Studies informing farm-habitat design should consider individual species’ preferences for effective enhancement of pest control services. Diverse cropping and landscape heterogeneity at the farm scale can benefit the varied preferences of individual species, help build diverse communities and, potentially increase service resilience and stability over time

Communicating the uncertainty in estimated greenhouse gas emissions from agriculture

In an effort to mitigate anthropogenic effects on the global climate system, industrialised countries are required to quantify and report, for various economic sectors, the annual emissions of greenhouse gases from their several sources and the absorption of the same in different sinks. These estimates are uncertain, and this uncertainty must be communicated effectively, if government bodies, research scientists or members of the public are to draw sound conclusions. Our interest is in communicating the uncertainty in estimates of greenhouse gas emissions from agriculture to those who might directly use the results from the inventory. We tested six methods of communication. These were: a verbal scale using the IPCC calibrated phrases such as ‘likely’ and ‘very unlikely’; probabilities that emissions are within a defined range of values; confidence intervals for the expected value; histograms; box plots; and shaded arrays that depict the probability density of the uncertain quantity. In a formal trial we used these methods to communicate uncertainty about four specific inferences about greenhouse gas emissions in the UK. Sixty four individuals who use results from the greenhouse gas inventory professionally participated in the trial, and we tested how effectively the uncertainty about these inferences was communicated by means of a questionnaire. Our results showed differences in the efficacy of the methods of communication, and interactions with the nature of the target audience. We found that, although the verbal scale was thought to be a good method of communication it did not convey enough information and was open to misinterpretation. Shaded arrays were similarly criticised for being open to misinterpretation, but proved to give the best impression of uncertainty when participants were asked to interpret results from the greenhouse gas inventory. Box plots were most favoured by our participants largely because they were particularly favoured by those who worked in research or had a stronger mathematical background. We propose a combination of methods should be used to convey uncertainty in emissions and that this combination should be tailored to the professional grou

Facilitating the elicitation of beliefs for use in Bayesian Belief modelling

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

Auditory artificial grammar learning in macaque and marmoset monkeys.

Artificial grammars (AG) are designed to emulate aspects of the structure of language, and AG learning (AGL) paradigms can be used to study the extent of nonhuman animals' structure-learning capabilities. However, different AG structures have been used with nonhuman animals and are difficult to compare across studies and species. We developed a simple quantitative parameter space, which we used to summarize previous nonhuman animal AGL results. This was used to highlight an under-studied AG with a forward-branching structure, designed to model certain aspects of the nondeterministic nature of word transitions in natural language and animal song. We tested whether two monkey species could learn aspects of this auditory AG. After habituating the monkeys to the AG, analysis of video recordings showed that common marmosets (New World monkeys) differentiated between well formed, correct testing sequences and those violating the AG structure based primarily on simple learning strategies. By comparison, Rhesus macaques (Old World monkeys) showed evidence for deeper levels of AGL. A novel eye-tracking approach confirmed this result in the macaques and demonstrated evidence for more complex AGL. This study provides evidence for a previously unknown level of AGL complexity in Old World monkeys that seems less evident in New World monkeys, which are more distant evolutionary relatives to humans. The findings allow for the development of both marmosets and macaques as neurobiological model systems to study different aspects of AGL at the neuronal level

Dynamics of bacterial blight disease in resistant and susceptible rice varieties

Bacterial blight (X. oryzae pv. oryzae) is a serious disease in rice across the world. To better control the disease, it is important to understand its epidemiology and how key aspects of this (e.g. infection efficiency, and spatial spread) change according to environment (e.g. local site conditions and season), management, and in particular, variety resistance. To explore this, we analysed data on the disease progress on resistant and susceptible varieties of rice grown at four sites in the Philippines across five seasons using a combination of mechanistic modelling and statistical analysis. Disease incidence was generally lower in the resistant variety. However, we found no evidence that the primary infection efficiency was lower in resistant varieties, suggesting that differences were largely due to reduced secondary spread. Despite secondary spread being attributed to splash dispersal which is exacerbated by wind and rain, the wetter sites of Pila and Victoria in south Luzon tended to have lower infection rates than the drier sites in central Luzon. Likewise, we found spread in the dry season can be substantial and should therefore not be ignored. In fact, we found site to be a greater determinant of the number of infection attempts suggesting that other environmental and management factors had greater effect on the disease than climate. Primary infection was characterised by spatially-random observations of disease incidence. As the season progressed, we observed an emerging short-range (1.6 m-4 m) spatial structure suggesting secondary spread was predominantly short-range, particularly where the resistant variety was grown

Model-based optimization of agricultural profitability and nutrient management: a practical approach for dealing with issues of scale

To manage agricultural landscapes more sustainably we must understand and quantify the synergies and trade-offs between environmental impact, production and other ecosystem services. Models play an important role in this type of analysis as generally it is infeasible to test multiple scenarios by experiment. These models can be linked with algorithms that optimise for multiple objectives by searching a space of allowable management interventions (the control variables). Optimisation of landscapes for multiple objectives can be computationally challenging, however, particularly if the scale of management is typically smaller (e.g. field-scale) than the scale at which the objective is quantified (landscape scale) resulting in a large number of control variables whose impacts do not necessarily scale linearly. In this paper, we explore some practical solutions to this problem through a case study. In our case study we link a relatively detailed, agricultural landscape model with a multiple-objective optimisation algorithm to determine solutions that both maximise on profitability and minimise greenhouse gas emissions in response to management. The optimisation algorithm combines a non-dominated sorting routine with differential evolution, whereby a “population” of 100 solutions evolve over time to a Pareto optimal front. We show the advantages of using a hierarchical approach to the optimisation, whereby it is applied to finer scale units first (i.e. fields), and then the solutions from each optimisation are combined in a second step to produce landscape-scale outcomes. We show that if there is no interaction between units then the solution derived using such an approach will be the same as the one obtained if the landscape is optimised in one step. However, if there is spatial interaction, or if there are constraints on the allowable sets of solutions then outcomes can be quite different. In these cases, other approaches to increase the efficiency of the optimisation may be more appropriate – such as initialising the control variables for half of the population of solutions with values expected to be near optimal. Our analysis shows the importance of aligning a policy or management recommendation with the appropriate scale

Promoting dietary changes for achieving health and sustainability targets

Globally, about 21–37% of total greenhouse gas (GHG) emissions are attributable to food systems. Dietary-related non-communicable diseases have increased significantly from 1990–2019 at a global scale. To achieve carbon emissions targets, increase resilience, and improve health there is a need to increase the sustainability of agricultural practises and change dietary habits. By considering these challenges together and focusing on a closer connection between consumers and sustainable production, we can benefit from a positive interaction between them. Using the 2019 EAT Lancet Commission dietary guidelines, this study analysed interview data and food diaries collected from members of Community Supported Agriculture (CSA) schemes and the wider UK population. By comparing the environmental sustainability and nutritional quality of their respective diets, we found that CSA members consumed diets closer to the EAT Lancet recommendations than controls. We identified significant differences in daily intakes of meat; dairy; vegetables; legumes; and sugar, and the diets of CSA members emitted on average 28% less CO2 compared to controls. We propose that agricultural and wider social and economic policies that increase the accessibility of CSAs for a more diverse demographic could support achieving health, biodiversity, and zero-emission policy targets