Woodland planting on UK pasture land is not economically feasible, yet is more profitable than some traditional farming practices

Abstract Increasing ecosystem service provision is a key strategy of the UK’s ongoing agricultural and environmental policy reforms. Enhancing forest cover by 4%, particularly on the least productive agricultural land, aims to maximise carbon sequestration and achieve net zero by 2050. Multiple factors affect the sequestration potential of afforestation schemes and landowner participation in them, highlighting the need for spatially explicit research. We used the InVEST Carbon Model to investigate the Loddon Catchment, southeast England as a study area. We assessed the carbon sequestration potential and economic feasibility of three broadleaved woodland planting scenarios; arable, pasture, and stakeholder-approved (SA) scenario. We found that over a 50-year time horizon, woodland planting on arable land has the greatest sequestration potential (4.02 tC ha−1 yr−1), compared to planting on pasture land (3.75 tC ha−1 yr−1). When monetising carbon sequestration at current market rates, woodland planting on agricultural land incurs a loss across all farm types. However, when including the value of unpaid labour, lowland pasture farms presently incur a greater loss (−€285.14 ha−1 yr−1) than forestry (−€273.16 ha−1 yr−1), making forestry a more economical land use. Subsidising up to the social value of carbon (€342.23 tC−1) significantly reduces this loss and may make afforestation of pasture land more appealing to farmers. Woodland planting on lowland pasture land would increase forest cover by up to 3.62%. However, due to the influence of farmer attitudes on participation, it is more realistic for afforestation to occur on lowland pasture land in the SA scenario, equating to a 0.74% increase

Soil osmotic potential and its effect on vapor flow from a pervaporative irrigation membrane

Pervaporative irrigation is a membrane technology that can be used for desalination and subsurface irrigation simultaneously. To irrigate, the tube-shaped polymer membrane is buried in soil and filled with water. Due to the membrane transport process, water enters the soil in vapor phase, drawn across the membrane when the relative humidity in the air-filled pores is low. Soils are typically humid environments, however, the presence of hygroscopic compounds such as fertilisers decreases the humidity. For example, at 20oC the humidity in air in equilibrium above a saturated ammonium nitrate solution is 63%. Here, experiments showed that the presence of fertilisers in sand increased the water flux across the membrane by an order of magnitude. An expression for vapor sorption into sand containing different hygroscopic compounds was developed and combined with a model of vapor and liquid flow in soil. The success of the model in simulating experimental results suggests that the proposed mechanism, adsorption of moisture from the vapor phase by hygroscopic compounds, explains the observed increase in the flux from the irrigation system

On the origin of carbon dioxide released from rewetted soils

When dry soils are rewetted a pulse of CO2 is invariably released, and whilst this phenomenon has been studied for decades, the precise origins of this CO2 remain obscure. We postulate that it could be of chemical (i.e. via abiotic pathways), biochemical (via free enzymes) or biological (via intact cells) origin. To elucidate the relative contributions of the pathways, dry soils were either sterilised (double autoclaving) or treated with solutions of inhibitors (15% trichloroacetic acid or 1% silver nitrate) targeting the different modes. The rapidity of CO2 release from the soils after the drying:rewetting (DRW) cycle was remarkable, with maximal rates of evolution within 6 min, and 41% of the total efflux over 96 h released within the first 24 h. The complete cessation of CO2 eflux following sterilisation showed there was no abiotic (dissolution of carbonates) contribution to the CO2 release on rewetting, and clear evidence for an organismal or biochemical basis to the flush. Rehydration in the presence of inhibitors indicated that there were approximately equal contributions from biochemical (outside membranes) and organismal (inside membranes) sources within the first 24 h after rewetting. This suggests that some of the flux was derived from microbial respiration, whilst the remainder was a consequence of enzyme activity, possibly through remnant respiratory pathways in the debris of dead cells

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

Applying cover crop residues as diverse mixtures increases initial microbial assimilation of crop residue-derived carbon

Increasing the diversity of the crops grown in arable soils delivers multiple ecological functions. Whether mixtures of residues from different crops grown in polyculture contribute to microbial assimilation of C to a greater extent than would be expected from applying individual residues is currently unknown. In this study, we used 13C isotope labelled cover crop residues (buckwheat, clover, radish, and sunflower) to track microbial assimilation of plant residue-derived C using phospholipid fatty acid (PLFA) analysis. We also quantified microbial assimilation of C derived from the soil organic matter (SOM) because fresh residue inputs also prime the decomposition of SOM. To consider the initial stages of residue decomposition, and preclude microbial turnover, we compared a quaternary mixture of residues with the average effect of their four components one day after incorporation. Our results show that the microbial biomass C (MBC) in the treatment receiving the mixed residue was significantly greater, by 132% (3.61 µg C g-1), than the mean plant residue-derived MBC in treatments receiving the four individual components of the mixture. However, there was no evidence that the mixture resulted in any additional assimilation of C derived from native SOM than the average observed in individual residue treatments. We surmise that, during the initial stages of crop residue decomposition, a greater biodiversity of residues increases microbial assimilation to a greater extent than would be expected from applying individual residues either due to faster decomposition or greater carbon use efficiency (CUE). This might be facilitated by functional complementarity in the soil microbiota permitted by a greater diversity of substrates, reducing competition for any single substrate. Therefore, growing and incorporating crop polycultures (e.g., cover crop mixtures) could be an effective method to increase microbial C assimilation in the early stages of cover crop decomposition

Resilience and food security: rethinking an ecological concept

1. Focusing on food production, in this paper we define resilience in the food security context as maintaining production of sufficient and nutritious food in the face of chronic and acute environmental perturbations. In agri-food systems, resilience is manifest over multiple spatial scales: field, farm, regional and global. Metrics comprise production and nutritional diversity as well as socio-economic stability of food supply. 2. Approaches to enhancing resilience show a progression from more ecologically-based methods at small scales to more socially-based interventions at larger scales. At the field scale, approaches include the use of mixtures of crop varieties, livestock breeds and of forage species, polycultures, and boosting ecosystem functions. Stress-tolerant crops, or with greater plasticity, provide technological solutions. 3. At the farm scale, resilience may be conferred by diversifying crops and livestock and by farmers implementing adaptive approaches in response to perturbations. Biodiverse landscapes may enhance resilience, but the evidence is weak. At regional to global scales, resilient food systems will be achieved by coordination and implementation of resilience approaches among farms, advice to farmers and targeted research. 4. Synthesis. Threats to food production are predicted to increase under climate change and land degradation. Holistic responses are needed that integrate across spatial scales. Ecological knowledge is critical, but should be implemented alongside agronomic solutions and socio-economic transformations

Cover crop residue diversity enhances microbial activity and biomass with additive effects on microbial structure

Cover crops have been widely used in agroecosystems to improve soil fertility and environmental sustainability. The decomposition of cover crop residues can have further effects on belowground communities and their activity, which is important for a series of soil functions (e.g., nutrient cycling and organic matter decomposition). We tested the effect of plant residues from a range of cover crop species on soil microbial activity and community assemblage. We predicted that cover crop residues would alter the soil microbial community and that a greater diversity of residues would enhance microbial decomposition. In an incubation study, we assessed the effect of crop residue diversity on microbial activity (soil respiration) and its consequent effects on microbial community composition (PLFA). We used either a biodiverse mixture of four cover crop residues (buckwheat, clover, sunflower, and radish) or an equal mass of the residues of each of the individual species. Cover crop residue incorporation significantly (P < 0.001) increased soil respiration during 84 days’ incubation and this universal response caused a significant change in microbial community composition by increasing the proportion of fungi and Gram-positive bacteria at the cost of decreasing Gram-negative bacteria. The diverse mixture of cover crop residues had a significantly (P < 0.05) greater soil respiration rate, by 57.61 µg C g-1 h-1, than the average of the four individual residues, but did not have a significantly different soil microbial biomass or microbial community structure. This finding could be attributed to a greater diversity of organic resources increasing the number biochemical niches, and hence activating dormant microbial communities to increase microbial activity without affecting microbial biomass or community composition. Greater respiration from similar microbial biomasses suggests that microbial activity might be more efficient after a more diverse substrate input. This study confirms the positive impact of cover crop residues on soil microbial biomass and activity and highlights that mixtures of cover crop residues may deliver enhanced soil functions beyond the sum of individual cover crop residues

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 on 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 optimisation 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

Assessing the impact of climate change on sweet potato in Uganda

Sweet potato is a mainstay of household food security and a major source of vitamin A across sub-Saharan Africa, and particularly in Uganda. Understanding how climate change is likely to impact on sweet potato would be useful for policymakers in Uganda making decisions to improve food security and increase resilience to climate shocks. However, sweet potato is an under-researched crop and the impacts of climate change have not been systematically analysed. The Sweet Potato Catalyst Project aims to assess the impacts of climate change on sweet potato in Uganda and develop ways for local stakeholders to access and assess this information to strengthen governance. This policy briefing note provides an overview of the research, the approach being taken and anticipated outcomes that will feed into the UNFCCC Koronovia Joint Work on Agriculture

Embedding expert opinion in a Bayesian network model to predict wheat yield from spring-summer weather

Wheat yield is highly dependent on weather, Therefore, predicting its effect can improve crop management decisions. Various modelling approaches have been used to predict wheat yield including process-based modelling, statistical models, and machine learning. However, these models typically require a large data set for training or fitting. They often also have a limited ability in capturing the effects of small-scale variability, time, and duration of extreme weather events. Here, we develop a Bayesian Network (BN) model by interviewing experts including farmers, embedding their knowledge from years of experience within a quantitative model. These experts identified the period from the beginning of anthesis to the end of grain filling stage as a critical period and maximum temperature, mean temperature and precipitation as key weather variables for inclusion in the BN. To keep the time input from experts manageable, the conditional probability table for the BN was constructed based on their anticipated impact on the mean yield of different weather conditions. The model predicted the yield in the same or neighbouring class (very low, low, medium, high and very high) as the reported yield with low error rate ranging from 9.1 to 15.2% and, when used to estimate the median predicted yield, R2 ranging from 41 to 52%. Interestingly, model successfully predicted the yield in years 1998, 2007, 2012 and 2020 which had the most extreme weather events. Additionally, the more recent data, from 2012 to 2022 was predicted more accurately, especially 2022 season which was not sown yet when eliciting information and recently added to the testing data. Little difference was observed between the predictions made using model parameters based only the opinion of the farm manager from which the test data originated, and the predictions made using the average opinion of a group of 9 experts. The inclusion of causal variables in the model also provided insight into the experts’ rationale, allowing unexpected results to be explored. This methodology provides a means to rapidly develop a successful predictive model of wheat yield with limited (or no) data using expert understanding. This model could be tuned and updated with data as it becomes available