An Integrated Economic, Environmental and Social Approach to Agricultural Land-Use Planning

Designing mosaic landscapes for sustainable outcome: Evaluating land-use options on ecosystem service provisioning in southwestern Ghana

The landscape in southwestern Ghana faces rampant modification due to socio-economic activities, posing threats to ecosystem service provision and environmental sustainability. Addressing these threats involves empowering land-use actors to design landscapes that offer multiple benefits concurrently. This study employs a geodesign framework, integrating participatory ecosystem service assessment and spatial simulations. This geodesign framework aims to design the landscape in a collaborative manner in a way that supports multiple benefits concurrently, mitigating the threats posed by landscape modification. Reflecting on local land-use perceptions during a workshop, we developed land-use options and land management strategies based on selected land-cover types. We identified urban greens, open space restoration, rubber mixed-stands, mangrove restoration, selective-cutting land preparation, soil conservation, and relay cropping as land-use options to target selected land-cover types of shrubland, cropland, smallholder rubber, smallholder palm, wetland, and settlement. The land management strategies translated into landscape scenarios based on local need conditions. We generated the local need conditions which translated into the landscape scenarios by reflecting on the location of land-cover types, 'change-effect' conditions within rubber, settlement, and cropland, and 'no-change'conditions within cropland. Results indicate synergies between the created landscape scenarios and ecosystem service provisioning, with 'no-change' within cropland providing the highest synergy and 'change-effect' within rubber providing the least synergy. Spatial modeling of local perceptions forms the novelty of this study, as the fusion of participatory assessments and spatial modeling allows for a more holistic understanding of the landscape, its services, and the potential implications of different management strategies. The geodesign framework facilitated the design of the complex heterogeneous landscape to visualize possibilities of maximizing multiple benefits and can be used for future planning on the landscape.

Crop sensitivity to waterlogging mediated by soil temperature and growth stage

Waterlogging constrains crop yields in many regions around the world. Despite this, key drivers of crop sensitivity to waterlogging have received little attention. Here, we compare the ability of the SWAGMAN Destiny and CERES models in simulating soil aeration index, a variable contemporaneously used to compute three distinct waterlogging indices, denoted hereafter as WI Destiny, WIASD1, and WIASD2. We then account for effects of crop growth stage and soil temperature on waterlogging impact by introducing waterlogging severity indices, WI Growth, which accommodates growth stage tolerance, and WI Plus, which accounts for both soil temperature and growth stage. We evaluate these indices using data collected in pot experiments with genotypes "Yang mai 11" and "Zheng mai 7698" that were exposed to both single and double waterlogging events. We found that WI Plus exhibited the highest correlation with yield (-0.82 to -0.86) suggesting that waterlogging indices which integrate effects of temperature and growth stage may improve projections of yield penalty elicited by waterlogging. Importantly, WI Plus not only allows insight into physiological determinants, but also lends itself to remote computation through satellite imagery. As such, this index holds promise in scalable monitoring and forecasting of crop waterlogging.

Eco-physiology and environmental impacts of newly developed rice genotypes for improved yield and nitrogen use efficiency coordinately

Significant advancements have been made in understanding the genetic regulation of nitrogen use efficiency (NUE) and identifying crucial NUE genes in rice. However, the development of rice genotypes that simultaneously exhibit high yield and NUE has lagged behind these theoretical advancements. The grain yield, NUE, and greenhouse gas (GHG) emissions of newly-bred rice genotypes under reduced nitrogen application remain largely unknown. To address this knowledge gap, field experiments were conducted, involving 80 indica (14 to 19 rice genotypes each year in Wuxue, Hubei) and 12 japonica (8 to 12 rice genotypes each year in Yangzhou, Jiangsu). Yield, NUE, agronomy, and soil parameters were assessed, and climate data were recorded. The experiments aimed to assess genotypic variations in yield and NUE among these genotypes and to investigate the eco-physiological basis and environmental impacts of coordinating high yield and high NUE. The results showed significant variations in yield and NUE among the genotypes, with 47 genotypes classified as moderate-high yield with high NUE (MHY_HNUE). These genotypes demonstrated the higher yields and NUE levels, with 9.6 t ha-1, 54.4 kg kg-1, 108.1 kg kg-1, and 64 % for yield, NUE for grain and biomass production, and N harvest index, respectively. Nitrogen uptake and tissue concentration were key drivers of the relationship between yield and NUE, particularly N uptake at heading and N concentrations in both straw and grain at maturity. Increase in pre-anthesis temperature consistently lowered yield and NUE. Genotypes within the MHY_HNUE group exhibited higher methane emissions but lower nitrous oxide emissions compared to those in the low to middle yield and NUE group, resulting in a 12.8 % reduction in the yield-scaled greenhouse gas balance. In conclusion, prioritizing crop breeding efforts on yield and resource use efficiency, as well as developing genotypes resilient to high temperatures with lower GHGs, can mitigate planetary warming.

Cropland carbon stocks driven by soil characteristics, rainfall and elevation

Soil organic carbon (SOC) can influence atmospheric CO₂ concentration and then the extent to which the climate emergency is mitigated globally. It follows the elucidation of the driving factors of cropland SOC stocks, which is fundamental to reducing soil carbon loss and promoting soil carbon sequestration. Here, we examined the influence of 16 environmental variables on SOC stocks and sequestration based on three machine learning soil mapping methods, i.e. multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBOOST), with 2875 observed soil samples from cropland topsoil across Hunan Province, China in 2010. We employed a structural equation model (SEM) to extricate the driving mechanisms of environmental variables on SOC stocks at the regional scale. Our results show that XGBOOST had the most reliable performance in predicting SOC stocks, explaining 66 % of the total SOC stock variation. Croplands with high SOC stocks were distributed in low-altitude and water-sufficient areas. The partial dependence of SOC on precipitation showed a trend of increasing and then slowly decreasing. In addition, the grid-based SEM results clearly presented the direct and indirect routes of environmental variables' impacts on cropland SOC stocks. Soil properties regulated by elevation, were the most influential natural factor on SOC stocks. Precipitation and elevation drove SOC stocks through direct and indirect effects respectively. Our SEM combined with machine learning approach can provide an effective explanation of the driving mechanism for SOC accumulation. We expect our proposed modelling approach can be applied to other regions and offer new insights, as a reference for mitigating cropland soil carbon loss under climate emergency conditions.

Coupling optimization with territorial LCA to support agricultural land-use planning

The life cycle assessment framework was adapted to the territorial level (the "territorial LCA") to assess the environmental impacts and services of land-use planning scenarios. Given the various geographical conditions of the territory, the potential alternatives of land-use scenarios could be enormous. To prevent the iterative process of proposing and comparing alternative scenarios, this work aims to move one step further to automatically generate optimal planning scenarios by linking the novel territorial LCA with multi-objective optimization (MOO). A fuzzy optimization approach is adopted to deal with the trade-offs among objectives and to generate optimized scenarios, minimizing the environmental damages and maximizing the satisfaction level of the desired land-use functions subjected to constraints such as area availability and demand. Geographical Information System (GIS) is employed to manipulate geographic datasets for spatial assessment. An illustrative case study tests the novel integrated method (the territorial LCA, MOO, and GIS) on its ability to propose optimal land-use planning for bioenergy production in a region in Belgium. The study results reveal the competition of land uses for different energy products, the trade-offs among impact categories, and potential impacts on other territories if implementing optimal land planning for the territory under study. The optimization outcomes can help decision-making on the optimal locations for different crop types (i.e., miscanthus, willow, and maize in the case study) and utilizations (i.e., electricity, heat, biogas, and bioethanol in this study) complying with the objectives and constraints. This integrated tool holds the potential to assist policymakers when deciding on how to use the territory facing the global context of increasing demands for multiple uses of bio-based products, such as for food, feed, fuel, fiber, and chemicals. Limitations of the current method and its potential for real-world applications are discussed, such as expanding the scope to include life cycle sustainability assessment and taking farmers' behavior and crop rotation into account.