Evaluating socio-economic and environmental sustainability of the sheep farming activity in Greece: a whole-farm mathematical programming approach

Ruminant livestock farming is an important agricultural activity, mainly located in less favoured areas. Furthermore, ruminants have been identi fi ed as a signi fi cant source of GHG emissions. In this study, a whole-farm optimization model is used to assess the socio-economic and environmental performance of the dairy sheep farming activity in Greece. The analysis is undertaken in two sheep farms that represent the extensive and the semi-intensive farming systems. Gross margin and labour are regarded as socio-economic indicators and GHG emissions as environmental indicators. The issue of the marginal abatement cost is also addressed. The results indicate that the semi-intensive system yields a higher gross margin/ewe (179 €) than the extensive system (117 €) and requires less labour. The extensive system causes higher emissions/kg of milk than the semi-intensive system (5.45 and 2.99 kg of CO2 equivalents, respectively). In both production systems, abatement is achieved primarily via reduction of the fl ock size and switch to cash crops. However, the marginal abatement cost is much higher in the case of the semi-intensive farms, due to their high productivity

Impact of climate variability on pineapple production in Ghana

Background: Climate variations have a considerable impact on crop production. For pineapple, variable temperatures and rainfall patterns are implicated, yet there is limited knowledge of the conditions and consequences of such variations. Pineapple production plays a major role in Ghana, primarily via socioeconomic impacts and the export economy. The aims of this study were to assess the impact of current climatic trends and variations in four pineapple growing districts in Ghana to provide stakeholders, particularly farmers, with improved knowledge for guidance in adapting to changing climate. Results: Trend analysis, standardized anomaly, correlation analysis as well as focus group discussions were employed to describe climate and yields as well as assess the relationship between climate and pineapple production from 1995 to 2014. The results revealed that, relative to Ga district, temperature (minimum and maximum) in the study areas was increasing over this period at a rate of up to 0.05 °C. Rainfall trends increased in all but Nsawam Adoagyiri district. Rainfall and temperature had different impacts on production, and pineapple was particularly sensitive to minimum temperature as accounting for up to 82% of yield variability. Despite consistent report of rainfall impact on growth stages later affecting quantity and quality of fruits, minimal statistical significance was found between rainfall and yield. Conclusions: With continuously increasing stresses imposed by a changing climate, the sustainability of pineapple production in Ghana is challenged. This subsequently has detrimental impacts on national employment and exports capacity resulting in increased poverty. Further research to explore short- and long-term adaption options in response to challenging conditions in the pineapple industry in Ghana is suggested

Integrating climate change mitigation and adaptation in agriculture and forestry: opportunities and trade-offs

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.International audienceAlthough many activities can jointly contribute to the climate change strategies of adaptation and mitigation, climate policies have generally treated these strategies separately. In recent years, there has been a growing interest shown by practitioners in agriculture, forestry, and landscape management in the links between the two strategies. This review explores the opportunities and trade-offs when managing landscapes for both climate change mitigation and adaptation; different conceptua-lizations of the links between adaptation and mitigation are highlighted. Under a first conceptualization of 'joint outcomes,' several reviewed studies analyze how activities without climatic objectives deliver joint adaptation and mitigation outcomes. In a second conceptualization of 'unintended side effects,' the focus is on how activities aimed at only one climate objective—either adaptation or mitigation—can deliver outcomes for the other objective. A third conceptualization of 'joint objectives' highlights that associating both adaptation and mitigation objectives in a climate-related activity can influence its outcomes because of multiple possible interactions. The review reveals a diversity of reasons for mainstreaming adaptation and mitigation separately or jointly in landscape management. The three broad conceptualizations of the links between adaptation and mitigation suggest different implications for climate policy mainstreaming and integration

Data requirements for crop modelling-Applying the learning curve approach to the simulation of winter wheat flowering time under climate change

A prerequisite for application of crop models is a careful parameterization based on observational data. However, there are limited studies investigating the link between quality and quantity of observed data and its suitability for model parameterization. Here, we explore the interactions between number of measurements, noise and model predictive skills to simulate the impact of 2050′s climate change (RCP8.5) on winter wheat flowering time. The learning curve of two winter wheat phenology models is analysed under different assumptions about the size of the calibration dataset, the measurement error and the accuracy of the model structure. Our assessment confirms that prediction skills improve asymptotically with the size of the calibration dataset, as with statistical models. Results suggest that less precise but larger training datasets can improve the predictive abilities of models. However, the non-linear relationship between number of measurements, measurement error, and prediction skills limit the compensation between data quality and quantity. We find that the model performance does not improve significantly with a theoretical minimum size of 7–9 observations when the model structure is approximate. While simulation of crop phenology is critical to crop model simulation, more studies are needed to explore data needs for assessing entire crop models

Dopaminergic Activation of Estrogen Receptors Induces Fos Expression within Restricted Regions of the Neonatal Female Rat Brain

Steroid receptor activation in the developing brain influences a variety of cellular processes that endure into adulthood, altering both behavior and physiology. Recent data suggests that dopamine can regulate expression of progestin receptors within restricted regions of the developing rat brain by activating estrogen receptors in a ligand-independent manner. It is unclear whether changes in neuronal activity induced by dopaminergic activation of estrogen receptors are also region specific. To investigate this question, we examined where the dopamine D1-like receptor agonist, SKF 38393, altered Fos expression via estrogen receptor activation. We report that dopamine D1-like receptor agonist treatment increased Fos protein expression within many regions of the developing female rat brain. More importantly, prior treatment with an estrogen receptor antagonist partially reduced D1-like receptor agonist-induced Fos expression only within the bed nucleus of the stria terminalis and the central amygdala. These data suggest that dopaminergic activation of estrogen receptors alters neuronal activity within restricted regions of the developing rat brain. This implies that ligand-independent activation of estrogen receptors by dopamine might organize a unique set of behaviors during brain development in contrast to the more wide spread ligand activation of estrogen receptors by estrogen

Shifts in soil ammonia-oxidizing community maintain the nitrogen stimulation of nitrification across climatic conditions

This is the final version. Available on open access from Wiley via the DOI in this recordData availability statement: The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.20022878 (Zhang, Cheng, et al., 2023).Anthropogenic nitrogen (N) loading alters soil ammonia-oxidizing archaea (AOA) and bacteria (AOB) abundances, likely leading to substantial changes in soil nitrification. However, the factors and mechanisms determining the responses of soil AOA:AOB and nitrification to N loading are still unclear, making it difficult to predict future changes in soil nitrification. Herein, we synthesize 68 field studies around the world to evaluate the impacts of N loading on soil ammonia oxidizers and nitrification. Across a wide range of biotic and abiotic factors, climate is the most important driver of the responses of AOA:AOB to N loading. Climate does not directly affect the N-stimulation of nitrification, but does so via climate-related shifts in AOA:AOB. Specifically, climate modulates the responses of AOA:AOB to N loading by affecting soil pH, N-availability and moisture. AOB play a dominant role in affecting nitrification in dry climates, while the impacts from AOA can exceed AOB in humid climates. Together, these results suggest that climate-related shifts in soil ammonia-oxidizing community maintain the N-stimulation of nitrification, highlighting the importance of microbial community composition in mediating the responses of the soil N cycle to N loading.National Natural Science Foundation of ChinaEuropean Union Horizon 2020Aarhus University Research FoundationDanish Independent Research FoundationNordic Committee of Agriculture and Food ResearchNatural Environment Research Council (NERC)Pioneer Center for Research in Sustainable Agricultural Futures (Land-CRAFT)DNR

Biology, Methodology or Chance? The Degree Distributions of Bipartite Ecological Networks

The distribution of the number of links per species, or degree distribution, is widely used as a summary of the topology of complex networks. Degree distributions have been studied in a range of ecological networks, including both mutualistic bipartite networks of plants and pollinators or seed dispersers and antagonistic bipartite networks of plants and their consumers. The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network. The skewed degree distributions of bipartite mutualistic and antagonistic networks are usually assumed to show that ecological or co-evolutionary processes constrain the relative numbers of specialists and generalists in the network. I show that a simple null model based on the principle of maximum entropy cannot be rejected as a model for the degree distributions in most of the 115 bipartite ecological networks tested here. The model requires knowledge of the number of nodes and links in the network, but needs no other ecological information. The model cannot be rejected for 159 (69%) of the 230 degree distributions of the 115 networks tested. It performed equally well on the plant and animal degree distributions, and cannot be rejected for 81 (70%) of the 115 plant distributions and 78 (68%) of the animal distributions. There are consistent differences between the degree distributions of mutualistic and antagonistic networks, suggesting that different processes are constraining these two classes of networks. Fit to the MaxEnt null model is consistently poor among the largest mutualistic networks. Potential ecological and methodological explanations for deviations from the model suggest that spatial and temporal heterogeneity are important drivers of the structure of these large networks