Explore the vast range of titles available.

Land use and land cover change (LULCC) can affect the climate system by altering biophysical surface characteristics. At the local scale, climate regulating functions are co-determined by land cover composition and configuration, i.e. the proportions and the spatial arrangement of land cover types. However, research on the relationship between LULCC and climate often focuses individually either on compositional or configurational aspects. As a result, there is a gap in our knowledge about the spatiotemporal distribution of land cover composition and configuration patterns influencing the local climate regulating functions. Here, we used a range of LULCC metrics between 1992 and 2015 and applied Self-Organizing Maps to characterize dominant land cover composition and configuration trajectories in Europe. We then tested the climate relevance of the five most dominant trajectories with a high-resolution regional climate model. Land cover composition and configuration simultaneously changed in more than 20% of the European landmass, with cropland transition to forest patches and bare soil representing the major trajectory. Climate model simulations predict a general increase in the topsoil temperature due to only changes in land cover composition and configuration. All trajectories showed increasing topsoil temperature variability during the crop growing season, with forest transition trajectories showing a greater increase. Our findings demonstrate the relevance of changes in both land cover composition and configuration for the local climate and warrant further empirical and model-based research with an explicit focus on quantifying the effects of simultaneous changes in both these LULCC dimensions.

Land use and land cover change affects two climate-relevant landscape dimensions: land cover composition and its spatial configuration. While the climate impacts of changes in land cover composition are well documented, the role of changes in land cover configuration remains poorly understood. Here, we examine how configuration changes in cropland-forest landscapes affect the ecosystem´s climate-regulating functions by conducting a multi-scenario experiment. We use the Terrestrial System Modeling Platform (TSMP), a modeling framework to run coupled high-resolution regional climate models (RCMs) with a focus on terrestrial hydrology. We employ stylized landscapes as input for TSMP and combine machine learning with SHapley Additive exPlanations values, an explanatory AI technique, to disentangle direct and mediating contributions. Our results indicate that changes in land cover configuration influence topsoil and 2 m air temperatures when composition is kept constant. Greater forest fragmentation is associated with overall warming, particularly over cropland during the growing season. This can mainly be attributed to reduced wind speed, lower latent heat flux, and higher sensible heat flux. These findings support the hypothesis that increasing forest fragmentation reduces the landscape’s capacity to mitigate local climate. However, we also discuss potential biases towards warming effects in contemporary RCMs. Our study demonstrates the potential of RCMs to address key land system science questions and promote interdisciplinary research at the intersection of climate change and landscape management. The type of experiments conducted may help to leverage both modeling and observational studies to advance our knowledge about the climate-relevance of landscape characteristics and arrive at scalable adaptation strategies.

Sustainable Intensification (SI) practices offer adopters exploiting improvement potentials in environmental performance of farming, i.e. enhance ecosystem functionality, while maintaining productivity. This paper proposes a directional meta-frontier approach for measuring farms’ eco-efficiency and respective improvement potentials in the direction of farms’ ecological output for SI evaluation. We account for farms’ selection processes into SI using a behavioural model and rely on a matched sample for adopters and non-adopters of agronomic SI practices from the northern German Plain. We conclude that the SI adopters determined the sample’s system frontier and showed higher mean eco-efficiency, but that most farms in our sample did not fully exploit the improvement potentials in biodiversity as ecological outcome.

Phosphorus (P) is essential for agriculture; however, local P surpluses can have adverse environmental effects, such as eutrophication. Optimal P fertiliser use, therefore, means balancing these effects. Although P fluxes from soil to plants are key research areas, little is known about on-farm use of P fertilisers. As, typically, not all fields or crops are treated with P annually, the transferability of measurements for other nutrients, such as nitrogen, appears limited. This study aims to close this knowledge gap. On-farm P use was described using the farm records of 50 farms in five regions of northern Germany for the period of 2010 to 2018. All sources for P fertilisation were taken into account as either P mineral, P organic or P total. Two indicators for on-farm P use were suggested: frequency, which gives the percentage of the treated area, and amount, which gives the quantity used per ha of the treated area. The frequency for P total ranged from 55.9% to 93.1% of the total farm area being fertilised. Amounts between 24.8 and 41.6 kg ha−1 P total were applied on the treated area of the farm. The results supported the separation of the quantity and frequency in on-farm P use. No decrease in P use was found during the period investigated. Using mixed models, the results further show that explanatory variables, including the farm characteristics and crop choice, explain the substantial variations in P use. It is recommended for the example of Germany to establish an official digital database for P fertilisers that can be updated professionally and is mandatory for all documentation on P use.

The increasing demand for agricultural commodities for food and energy purposes has led to intensified agricultural land management, along with the homogenization of landscapes, adverse biodiversity effects and robustness of landscapes regarding the provision of ecosystem services. At the same time, subsidized organic agriculture and extensive grassland use supports the provision of ecosystem services. Yet little is understood about how to evaluate a landscape’s potential to contribute to protecting and enhancing biodiversity and ecosystem services. To address this gap, we use plot-level data from the Integrated Administration and Control System (IACS) for Germany’s federal state of Brandenburg, and based on a two-step cluster analysis, we identify six types of agricultural landscapes. These clusters differ in landscape structure, diversity and measures for agricultural land management intensity. Agricultural land in Brandenburg is dominated by high shares of cropland but fragmented differently. Lands under organic management and those with a high share of maize show strong spatial autocorrelation, pointing to local clusters. Identification of different types of landscapes permits locally- and region-adapted designs of environmental and agricultural policy measures improves outcome-oriented environmental policy impact evaluation and landscape planning. Our approach allows transferability to other EU regions.Analysis of (landscape) metrics indicating agricultural landscape structure, landscape diversity and management using plot-based agricultural data.Identification of agricultural landscape types through a two-step data-driven cluster analysis deriving six types for Brandenburg, Germany.Highly transferable approach and relevant results for better-tailored monitoring and policy implementation on local level.

The scientific production system is crucial in how global challenges are addressed. However, scholars have recently begun to voice concerns about structural inefficiencies within the system, as highlighted, for example, by the replication crisis, the p-value debate and various forms of publication bias. Most suggested remedies tend to address only partial aspects of the system's inefficiencies, but there is currently no unifying agenda in favour of an overall transformation of the system. Based on a critical review of the current scientific system and an exploratory pilot study about the state of student training, we argue that a unifying agenda is urgently needed, particularly given the emergence of artificial intelligence (AI) as a tool in scientific writing and the research discovery process. Without appropriate responses from academia, this trend may even compound current issues around credibility due to limited replicability and ritual-based statistical practice while amplifying all forms of existing biases. Naïve openness in the science system alone is unlikely to lead to major improvements. We contribute to the debate and call for a system reform by identifying key elements in the definition of transformation pathways towards open, democratic and conscious learning, teaching, reviewing and publishing supported by openly maintained AI tools. Roles and incentives within the review process will have to adapt and be strengthened in relation to those that apply to authors. Scientists will have to write less, learn differently and review more in the future, but need to be trained better in and for AI even today.

Precision farming systems promise a highly efficient resource use reducing cost for farmers and contributing to the preservation of the environment. A major obstacle, however, in such systems dissemination is the reluctant adoption by farmers. Prior work is suggesting that mainly knowledge or missing resources for investment are barriers, while social norms are rarely addressed for precision farming systems. We adopt the reasoned action approach including behavioural, social, and control aspects to analyse intentions and actual use of sustainable digital fertilisation methods. Based on a German sample of farmers, we find that social norm is the major predictor, while behavioural and control aspects surprisingly are not relevant at all. The results contribute to the understanding of what drives farmers in adopting precision farming systems on a theoretical basis and highlight the importance of considering social norms in increasing adoption.

Much of the land economics literature has largely ignored the spatial nature of competition and related differences between farmland rental and sales markets when assessing return rates from farming, the capitalization of agricultural, environmental and energy policy into land values, and climate change impacts. We propose a model for price formation in both markets under a spatial competition framework. We demonstrate that price formation differs, particularly under policy-induced output price shocks.We suggest that using the rent-price ratio as an approximation for expectations in the net returns of farming, based on the net present value model, may produce biased results. In consequence, studies relying on land prices need to control for local land competition, farming structure, and policies.

Much of the land economics literature has largely ignored the spatial nature of competition and related differences between farmland rental and sales markets when assessing return rates from farming, the capitalization of agricultural, environmental and energy policy into land values, and climate change impacts. We propose a model for price formation in both markets under a spatial competition framework. We demonstrate that price formation differs, particularly under policy-induced output price shocks.We suggest that using the rent-price ratio as an approximation for expectations in the net returns of farming, based on the net present value model, may produce biased results. In consequence, studies relying on land prices need to control for local land competition, farming structure, and policies.

Much of the land economics literature has largely ignored the spatial nature of competition and related differences between farmland rental and sales markets when assessing return rates from farming, the capitalization of agricultural, environmental and energy policy into land values, and climate change impacts. We propose a model for price formation in both markets under a spatial competition framework. We demonstrate that price formation differs, particularly under policy-induced output price shocks.We suggest that using the rent-price ratio as an approximation for expectations in the net returns of farming, based on the net present value model, may produce biased results. In consequence, studies relying on land prices need to control for local land competition, farming structure, and policies.