Grazing livestock move by Lévy walks: Implications for soil health and environment.

Grazing livestock plays an important role in the context of food security, agricultural sustainability and climate change. Understanding how livestock move and interact with their environment may offer new insights on how grazing practices impact soil and ecosystem functions at spatial and temporal scales where knowledge is currently limited. We characterized daily and seasonal grazing patterns using Global Positioning System (GPS) data from two grazing strategies: conventionally- and rotationally-grazed pastures. Livestock movement was consistent with the so-called Lévy walks, and could thus be simulated with Lévy-walk based probability density functions. Our newly introduced "Moovement model" links grazing patterns with soil structure and related functions by coupling animal movement and soil structure dynamics models, allowing to predict spatially-explicit changes in key soil properties. Predicted post-grazing management-specific bulk densities were consistent with field measurements and confirmed that rotational grazing produced similar disturbance as conventional grazing despite hosting higher stock densities. Harnessing information on livestock movement and its impacts in soil structure within a modelling framework can help testing and optimizing grazing strategies for ameliorating their impact on soil health and environment.

Absence of a home-field advantage within a short-rotation arable cropping system.

Aims: The home-field advantage (HFA) hypothesis predicts faster decomposition of plant residues in home soil compared to soils with different plants (away), and has been demonstrated in forest and grassland ecosystems. It remains unclear if this legacy effect applies to crop residue decomposition in arable crop rotations. Such knowledge could improve our understanding of decomposition dynamics in arable soils and may allow optimisation of crop residue amendments in arable systems by cleverly combining crop-residue rotations with crop rotations to increase the amount of residue-derived C persisting in soil. Methods: We tested the HFA hypothesis in a reciprocal transplant experiment with mesh bags containing wheat and oilseed rape residues in soils at three stages of a short-rotation cropping system. Subsets of mesh bags were retrieved monthly for six months to determine residue decomposition rates, concomitantly measuring soil available N, microbial community structure (phospholipid fatty acid analysis), and microbial activity (Tea Bag Index protocol) to assess how plants may influence litter decomposition rates via alterations to soil biochemical properties and microbial communities. Results: The residues decomposed at similar rates at all rotational stages. Thorough data investigation using several statistical approaches revealed no HFA within the crop rotation. Soil microbial community structures were similar at all rotational stages. Conclusions: We attribute the absence of an HFA to the shortness of the rotation and soil disturbance involved in intensive agricultural practices. It is therefore unlikely that appreciable benefits could be obtained in short conventionally managed arable rotations by introducing a crop-residue rotation. Supplementary Information: The online version contains supplementary material available at 10.1007/s11104-022-05419-z.

Exploring the effects of land management change on productivity, carbon and nutrient balance: Application of an Ensemble Modelling Approach to the upper River Taw observatory, UK.

Agriculture is challenged to produce healthy food and to contribute to cleaner energy whilst mitigating climate change and protecting ecosystems. To achieve this, policy-driven scenarios need to be evaluated with available data and models to explore trade-offs with robust accounting for the uncertainty in predictions. We developed a novel model ensemble using four complementary state-of-the-art agroecosystems models to explore the impacts of land management change. The ensemble was used to simulate key agricultural and environmental outputs under various scenarios for the upper River Taw observatory, UK. Scenarios assumed (i) reducing livestock production whilst simultaneously increasing the area of arable where it is feasible to cultivate (PG2A), (ii) reducing livestock production whilst simultaneously increasing bioenergy production in areas of the catchment that are amenable to growing bioenergy crops (PG2BE) and (iii) increasing both arable and bioenergy production (PG2A + BE). Our ensemble approach combined model uncertainty using the tower property of expectation and the law of total variance. Results show considerable uncertainty for predicted nutrient losses with different models partitioning the uncertainty into different pathways. Bioenergy crops were predicted to produce greatest yields from Miscanthus in lowland and from SRC-willow (cv. Endurance) in uplands. Each choice of management is associated with trade-offs; e.g. PG2A results in a significant increase of edible calories (6736 Mcal ha-1) but reduced soil C (-4.32 t C ha-1). Model ensembles in the agroecosystem context are difficult to implement due to challenges of model availability and input and output alignment. Despite these challenges, we show that ensemble modelling is a powerful approach for applications such as ours, offering benefits such as capturing structural as well as data uncertainty and allowing greater combinations of variables to be explored. Furthermore, the ensemble provides a robust means for combining uncertainty at different scales and enables us to identify weaknesses in system understanding.

Local impacts of climate change on winter wheat in Great Britain.

Under future CMIP5 climate change scenarios for 2050, an increase in wheat yield of about 10% is predicted in Great Britain (GB) as a result of the combined effect of CO2 fertilization and a shift in phenology. Compared to the present day, crops escape increases in the climate impacts of drought and heat stresses on grain yield by developing before these stresses can occur. In the future, yield losses from water stress over a growing season will remain about the same across Great Britain with losses reaching around 20% of potential yield, while losses from drought around flowering will decrease and account for about 9% of water limited yield. Yield losses from heat stress around flowering will remain negligible in the future. These conclusions are drawn from a modelling study based on the response of the Sirius wheat simulation model to local-scale 2050-climate scenarios derived from 19 Global Climate Models from the CMIP5 ensemble at 25 locations representing current or potential wheat-growing areas in GB. However, depending on susceptibility to water stress, substantial interannual yield variation between locations is predicted, in some cases suggesting low wheat yield stability. For this reason, local-scale studies should be performed to evaluate uncertainties in yield prediction related to future weather patterns.

The potential for soybean to diversify the production of plant-based protein in the UK.

Soybean (Glycine max) offers an important source of plant-based protein. Currently much of Europe's soybean is imported, but there are strong economic and agronomic arguments for boosting local production. Soybean is grown in central and eastern Europe but is less favoured in the North due to climate. We conducted field trials across three seasons and two sites in the UK to test the viability of early-maturing soybean varieties and used the data from these trials to calibrate and validate the Rothamsted Landscape Model. Once validated, the model was used to predict the probability soybean would mature and the associated yield for 26 sites across the UK based on weather data under current, near-future (2041-60) and far-future (2081-2100) climate. Two representative concentration pathways, a midrange mitigation scenario (RCP4.5) and a high emission scenario (RCP8.5) were also explored. Our analysis revealed that under current climate early maturing varieties will mature in the south of the UK, but the probability of failure increases with latitude. Of the 26 sites considered, only at one did soybean mature for every realisation. Predicted expected yields ranged between 1.39 t ha-1 and 1.95 t ha-1 across sites. Under climate change these varieties are likely to mature as far north as southern Scotland. With greater levels of CO2, yield is predicted to increase by as much as 0.5 t ha-1 at some sites in the far future, but this is tempered by other effects of climate change meaning that for most sites no meaningful increase in yield is expected. We conclude that soybean is likely to be a viable crop in the UK and for similar climates at similar latitudes in Northern Europe in the future but that for yields to be economically attractive for local markets, varieties must be chosen to align with the growing season.

Model-based optimisation 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 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 evolves 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.

Optimizing multifunctional agroecosystems in irrigated dryland agriculture to restore soil carbon - Experiments and modelling.

Irrigated dryland agroecosystems could become more sustainable if crop and soil management enhanced soil organic carbon (SOC). We hypothesized that combining high inputs from cover crops with no-tillage will increase long-term SOC stocks. Caatinga shrublands had been cleared in 1972 for arable crops and palm plantations before implementing field experiments on Mango and Melon systems (established in 2009 and 2012, respectively). Each of the two experiments were managed with no-till (NT) or conventional till (CT), and three types of cover cropping, either a plant mixture of 75% (PM1) or 25% (PM2) legumes, or spontaneous vegetation (SV). The RothC model was used with a daily timestep to simulate the soil moisture dynamics and C turnover for this dry climate. Carbon inputs were between 2.62 and 5.82 Mg C ha-1 year-1 and increased the depleted SOC stocks by 0.08 to 0.56 Mg C ha-1 year-1. Scenarios of continuous biomass inputs of ca. 5 Mg C ha-1 year-1 for 60 years are likely to increase SOC stocks in the mango NT beyond the original Caatinga SOC by between 19.2 and 20.5 Mg C ha-1. Under CT similar inputs would increase SOC stocks only marginally above depletion (2.75 to 2.47 Mg C ha-1). Under melon, annual carbon inputs are slightly greater (up to 5.5 Mg C ha-1 year-1) and SOC stocks would increase on average by another 8% to 22.3 to 20.6 Mg C ha-1 under NT and by 8 Mg C ha-1 under CT. These long-term simulations show that combining NT with high quality cover crops (PM1, PM2) would exceed SOC stocks of the initial Caatinga within 20 and 25 years under irrigated melon and mango cultivation, respectively. These results present a solution to reverse prior loss of SOC by replacing CT dryland agriculture with irrigated NT plus high input cover crops agroecosystems.

Accounting for data sparsity when forming spatially coherent zones.

Efficient farm management can be aided by the identification of zones in the landscape. These zones can be informed from different measured variables by ensuring a sense of spatial coherence. Forming spatially coherent zones is an established method in the literature, but has been found to perform poorly when data are sparse. In this paper, we describe the different types of data sparsity and investigate how this impacts the performance of established methods. We introduce a set of methodological advances that address these shortcomings to provide a method for forming spatially coherent zones under data sparsity.

Multi-objective optimization as a tool to identify possibilities for future agricultural landscapes.

Agricultural landscapes provide many functions simultaneously including food production, regulation of water and regulation of greenhouse gases. Thus, it is challenging to make land management decisions, particularly transformative changes, that improve on one function without unintended consequences for other functions. To make informed decisions the trade-offs between different landscape functions must be considered. Here, we use a multi-objective optimization algorithm with a model of crop production that also simulates environmental effects such as nitrous oxide emissions to identify trade-off frontiers and associated possibilities for agricultural management. Trade-offs are identified in three soil types, using wheat production in the UK as an example, then the trade-off for combined management of the three soils is considered. The optimization algorithm identifies trade-offs between different objectives and allows them to be visualised. For example, we observed a highly non-linear trade-off between wheat yield and nitrous oxide emissions, illustrating where small changes might have a large impact. We used a cluster analysis to identify distinct management strategies with similar management actions and use these clusters to link the trade-off curves to possibilities for management. There were more possible strategies for achieving desirable environmental outcomes and remaining profitable when the management of different soil types was considered together. Interestingly, it was on the soil capable of the highest potential profit that lower profit strategies were identified as useful for combined management. Meanwhile, to maintain average profitability across the soils, it was necessary to maximise the profit from the soil with the lowest potential profit. These results are somewhat counterintuitive and so the range of strategies supplied by the model could be used to stimulate discussion amongst stakeholders. In particular, as some key objectives can be met in different ways, stakeholders could discuss the impact of these management strategies on other objectives not quantified by the model.

Exploring Future Food Provision Scenarios for China.

Developing sustainable food systems is essential, especially for emerging economies, where food systems are changing rapidly and affect the environment and natural resources. We explored possible future pathways for a sustainable food system in China, using multiple environmental indicators linked to eight of the Sustainable Development Goals (SDGs). Forecasts for 2030 in a business as usual scenario (BAU) indicate increases in animal food consumption as well as increased shortages of the land available and the water needed to produce the required food in China. Associated greenhouse gas emissions and nitrogen and phosphorus losses could become 10-42% of global emissions in 2010. We developed three main pathways besides BAU [produce more and better food (PMB), consume and waste less food (CWL), and import more food (IMF)] and analyzed their impacts and contributions to achieving one or more of the eight SDGs. Under these scenarios, the demand for land and water and the emissions of GHG and nutrients may decrease by 7-55% compared to BAU, depending on the pathway followed. A combination of PMB and CWL was most effective, while IMF externalizes impacts to countries exporting to China. Modestly increasing feed or food imports in a selective manner could ease the pressure on natural resources. Our modeling framework allows us to analyze the effects of changes in food production-consumption systems in an integrated manner, and the results can be linked to the eight SDGs. Despite formidable technological, social, educational, and structural barriers that need to be overcome, our study indicates that the ambitious targets of China's new agricultural and environmental strategy appear to be achievable.

Sustainable development goal 2: Improved targets and indicators for agriculture and food security.

The pursuit of global food security and agricultural sustainability, the dual aim of the second sustainable development goal (SDG-2), requires urgent and concerted action from developing and developed countries. This, in turn, depends on clear and universally applicable targets and indicators which are partially lacking. The novel and complex nature of the SDGs poses further challenges to their implementation on the ground, especially in the face of interlinkages across SDG objectives and scales. Here we review the existing SDG-2 indicators, propose improvements to facilitate their operationalization, and illustrate their practical implementation in Nigeria, Brazil and the Netherlands. This exercise provides insights into the concrete actions needed to achieve SDG-2 across contrasting development contexts and highlights the challenges of addressing the links between targets and indicators within and beyond SDG-2. Ultimately, it underscores the need for integrated policies and reveals opportunities to leverage the fulfillment of SDG-2 worldwide.

The environmental costs and benefits of high-yield farming.

How we manage farming and food systems to meet rising demand is pivotal to the future of biodiversity. Extensive field data suggest impacts on wild populations would be greatly reduced through boosting yields on existing farmland so as to spare remaining natural habitats. High-yield farming raises other concerns because expressed per unit area it can generate high levels of externalities such as greenhouse gas (GHG) emissions and nutrient losses. However, such metrics underestimate the overall impacts of lower-yield systems, so here we develop a framework that instead compares externality and land costs per unit production. Applying this to diverse datasets describing the externalities of four major farm sectors reveals that, rather than involving trade-offs, the externality and land costs of alternative production systems can co-vary positively: per unit production, land-efficient systems often produce lower externalities. For GHG emissions these associations become more strongly positive once forgone sequestration is included. Our conclusions are limited: remarkably few studies report externalities alongside yields; many important externalities and farming systems are inadequately measured; and realising the environmental benefits of high-yield systems typically requires additional measures to limit farmland expansion. Yet our results nevertheless suggest that trade-offs among key cost metrics are not as ubiquitous as sometimes perceived.

Impact of two centuries of intensive agriculture on soil carbon, nitrogen and phosphorus cycling in the UK.

This paper describes an agricultural model (Roth-CNP) that estimates carbon (C), nitrogen (N) and phosphorus (P) pools, pool changes, their balance and the nutrient fluxes exported from arable and grassland systems in the UK during 1800-2010. The Roth-CNP model was developed as part of an Integrated Model (IM) to simulate C, N and P cycling for the whole of UK, by loosely coupling terrestrial, hydrological and hydro-chemical models. The model was calibrated and tested using long term experiment (LTE) data from Broadbalk (1843) and Park Grass (1856) at Rothamsted. We estimated C, N and P balance and their fluxes exported from arable and grassland systems on a 5km×5km grid across the whole of UK by using the area of arable of crops and livestock numbers in each grid and their management. The model estimated crop and grass yields, soil organic carbon (SOC) stocks and nutrient fluxes in the form of NH4-N, NO3-N and PO4-P. The simulated crop yields were compared to that reported by national agricultural statistics for the historical to the current period. Overall, arable land in the UK have lost SOC by -0.18, -0.25 and -0.08MgCha-1y-1 whereas land under improved grassland SOC stock has increased by 0.20, 0.47 and 0.24MgCha-1y-1 during 1800-1950, 1950-1970 and 1970-2010 simulated in this study. Simulated N loss (by leaching, runoff, soil erosion and denitrification) increased both under arable (-15, -18 and -53kgNha-1y-1) and grass (-18, -22 and -36kgNha-1y-1) during different time periods. Simulated P surplus increased from 2.6, 10.8 and 18.1kgPha-1y-1 under arable and 2.8, 11.3 and 3.6kgPha-1y-1 under grass lands 1800-1950, 1950-1970 and 1970-2010.

The landscape model: A model for exploring trade-offs between agricultural production and the environment.

We describe a model framework that simulates spatial and temporal interactions in agricultural landscapes and that can be used to explore trade-offs between production and environment so helping to determine solutions to the problems of sustainable food production. Here we focus on models of agricultural production, water movement and nutrient flow in a landscape. We validate these models against data from two long-term experiments, (the first a continuous wheat experiment and the other a permanent grass-land experiment) and an experiment where water and nutrient flow are measured from isolated catchments. The model simulated wheat yield (RMSE 20.3-28.6%), grain N (RMSE 21.3-42.5%) and P (RMSE 20.2-29% excluding the nil N plots), and total soil organic carbon particularly well (RMSE3.1-13.8%), the simulations of water flow were also reasonable (RMSE 180.36 and 226.02%). We illustrate the use of our model framework to explore trade-offs between production and nutrient losses.

Milled cereal straw accelerates earthworm (Lumbricus terrestris) growth more than selected organic amendments.

Earthworms benefit agriculture by providing several ecosystem services. Therefore, strategies to increase earthworm abundance and activity in agricultural soils should be identified, and encouraged. Lumbricus terrestris earthworms primarily feed on organic inputs to soils but it is not known which organic amendments are the most effective for increasing earthworm populations. We conducted earthworm surveys in the field and carried out experiments in single-earthworm microcosms to determine the optimum food source for increasing earthworm biomass using a selection of crop residues and organic wastes available to agriculture. We found that although farmyard manure increased earthworm populations more than cereal straw in the field, straw increased earthworm biomass more than manures when milled and applied to microcosms. Earthworm growth rates were positively correlated with the calorific value of the amendment and straw had a much higher calorific value than farmyard manure, greenwaste compost, or anaerobic digestate. Reducing the particle size of straw by milling to <3 mm made the energy in the straw more accessible to earthworms. The benefits and barriers to applying milled straw to arable soils in the field are discussed.

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 group.

The Effect of Farmers' Decisions on Pest Control with Bt Crops: A Billion Dollar Game of Strategy.

A farmer's decision on whether to control a pest is usually based on the perceived threat of the pest locally and the guidance of commercial advisors. Therefore, farmers in a region are often influenced by similar circumstances, and this can create a coordinated response for pest control that is effective at a landscape scale. This coordinated response is not intentional, but is an emergent property of the system. We propose a framework for understanding the intrinsic feedback mechanisms between the actions of humans and the dynamics of pest populations and demonstrate this framework using the European corn borer, a serious pest in maize crops. We link a model of the European corn borer and a parasite in a landscape with a model that simulates the decisions of individual farmers on what type of maize to grow. Farmers chose whether to grow Bt-maize, which is toxic to the corn borer, or conventional maize for which the seed is cheaper. The problem is akin to the snow-drift problem in game theory; that is to say, if enough farmers choose to grow Bt maize then because the pest is suppressed an individual may benefit from growing conventional maize. We show that the communication network between farmers' and their perceptions of profit and loss affects landscape scale patterns in pest dynamics. We found that although adoption of Bt maize often brings increased financial returns, these rewards oscillate in response to the prevalence of pests.

Advances in the understanding of nutrient dynamics and management in UK agriculture.

Current research on macronutrient cycling in UK agricultural systems aims to optimise soil and nutrient management for improved agricultural production and minimise effects on the environment and provision of ecosystem services. Nutrient use inefficiencies can cause environmental pollution through the release of greenhouse gases into the atmosphere and of soluble and particulate forms of N, P and carbon (C) in leachate and run-off into watercourses. Improving nutrient use efficiencies in agriculture calls for the development of sustainable nutrient management strategies: more efficient use of mineral fertilisers, increased recovery and recycling of waste nutrients, and, better exploitation of the substantial inorganic and organic reserves of nutrients in the soil. Long-term field experimentation in the UK has provided key knowledge of the main nutrient transformations in agricultural soils. Emerging analytical technologies, especially stable isotope labelling, that better characterise macronutrient forms and bioavailability and improve the quantification of the complex relationships between the macronutrients in soils at the molecular scale, are augmenting this knowledge by revealing the underlying processes. The challenge for the future is to determine the relationships between the dynamics of N, P and C across scales, which will require both new modelling approaches and integrated approaches to macronutrient cycling.

Physical effects of soil drying on roots and crop growth.

The nature and effect of the stresses on root growth in crops subject to drying is reviewed. Drought is a complex stress, impacting on plant growth in a number of interacting ways. In response, there are a number of ways in which the growing plant is able to adapt to or alleviate these stresses. It is suggested that the most significant opportunity for progress in overcoming drought stress and increasing crop yields is to understand and exploit the conditions in soil by which plant roots are able to maximize their use of resources. This may not be straightforward, with multiple stresses, sometimes competing functions of roots, and conditions which impact upon roots very differently depending upon what soil, what depth or what stage of growth the root is at. Several processes and the interaction between these processes in soil have been neglected. It is our view that drought is not a single, simple stress and that agronomic practice which seeks to adapt to climate change must take account of the multiple facets of both the stress induced by insufficient water as well as other interacting stresses such as heat, disease, soil strength, low nutrient status, and even hypoxia. The potential for adaptation is probably large, however. The possible changes in stress as a result of the climate change expected under UK conditions are assessed and it appears possible that wet warm winters will impact on root growth as much if not more than dry warm summers.