Farm Diversification in Relation to Landscape Properties

Current European Common Agricultural Policy (CAP) has been moving from production support subsidies to direct decoupled income support. The emergence in policy making of the concept of multifunctional agriculture leads to the recognition that a farmer produces more than food: he produces jointly both commodity and non-commodity goods. Environmental contracts were developed in order to encourage the provision of non-commodity goods such as landscape or biodiversity. Next to these contracts, other activities as for example recreation can be observed. They are the result of farm diversification. The role of location in farmers’ decision making to diversify is pointed out in literature but geographical information is generally reduced to the location within a political delimitation unit the empirical work. Objective of this paper is two-fold. Firstly, it addresses the role of location, in term of site specific natural conditions as well as neighbouring emerging dynamics in farmer’s decision making to diversify. Attention is paid to number of activities as well as the specific types of activities, notably green services, daily recreation and other farm-linked services. Secondly, this paper introduces income from agriculture explicitly allowing testing short term price sensitivity. It was found that attractive landscape is a driver for diversification as these landscape offer more opportunities. Furthermore, diversification is responsive to price. Thirdly, role of density of past multifunctional activities in the neighborhood influences farm diversification: multifunctional activities create an externality effects as new activities emerge next to already existing ones. This dynamic may lead to the emergence of ‘multifunctional hotspots’ in landscape.Farmer diversification, landscape services, location, Farm Management, Land Economics/Use,

Ideotyping integrated aquaculture systems to balance soil nutrients

Due to growing land scarcity and lack of nutrient inputs, African farmers switched from shifting cultivation to continuous cropping and extended crop area by bringing fragile lands such as river banks and hill slopes into production. This accelerated soil fertility decline caused by erosion, harvesting and insufficient nutrient replenishment. We explored the feasibility to reduce nutrient depletion by increasing nutrient utilization efficiencies, while diversifying and increasing food production through the development of integrated aquaculture – agriculture (IAA). Considering the climatic conditions prevailing in Kenyan highlands, aquaculture production scenarios were ideotyped per agro-ecological zone. These aquaculture production scenarios were integrated into existing NUTrient MONitoring (NUTMON) farm survey data for the area. The nutrient balances and flows of the resulting IAA-systems were compared to present land use. The effects of IAA development on nutrient depletion and total food production were evaluated. With the development of IAA systems, nutrient depletion rates dropped by 23–35%, agricultural production increased by 2–26% and overall farm food production increased by 22–70%. The study demonstrates that from a bio-physical point of view, the development of IAA-systems in Africa is technically possible and could raise soil fertility and total farm production. Further studies that evaluate the economic feasibility and impacts on the livelihood of farming households are recommended

Exploring the challenges with soil data in regional land use analysis

Over recent decades, environmental models have gradually replaced traditional, qualitative land evaluation in regional land use analysis (RLUA). This changed the data requirements as the environmental models require quantitative, high resolution and spatially exhaustive data. As resources to collect new data are limited, RLUA often relies on already existing data. These data often do not meet the data requirements for the environmental models. Hence, a gap developed between the supply and demand of data in RLUA. This study aims to explore and analyse the effect of using different soil datasets in a case study for Machakos and Makueni counties (Kenya). Six soil datasets were available for the study area and showed large differences. For example, average clay percentages varied between 11.7% and 44.4%. The soil datasets were developed under different assumptions on e.g., soil variability. Four assumptions were verified using a field survey. An ongoing RLUA, the Global Yield Gap Atlas (GYGA) project, was taken as a case study to analyse the effect of using different soil datasets. The GYGA project aims to assess yield gaps defined as the difference between potential or water-limited yields and actual yields. Rain-fed maize is the dominating cropping system in Machakos and Makueni counties. The GYGA project uses soil data for the selection of the most dominant maize growing areas and to simulate water-limited maize yields. The protocols developed by the GYGA project were applied to the six soil datasets. This resulted in the selection of six different maize-growing areas and different water-limited maize yields. Our study clearly demonstrates the large differences between soil datasets. Main challenges with soil data in RLUA are: i) understand the assumptions in soil datasets, ii) create soil datasets that meet the requirements for regional land use analysis, iii) not only rely on legacy soil data but also collect new soil data and iv) validate soil datasets

CDC2/SPDY transiently associates with endoplasmic reticulum exit sites during oocyte maturation

<p>Abstract</p> <p>Background</p> <p>Mammalian oocytes acquire competence to be fertilized during meiotic maturation. The protein kinase CDC2 plays a pivotal role in several key maturation events, in part through controlled changes in CDC2 localization. Although CDC2 is involved in initiation of maturation, a detailed analysis of CDC2 localization at the onset of maturation is lacking. In this study, the subcellular distribution of CDC2 and its regulatory proteins cyclin B and SPDY in combination with several organelle markers at the onset of pig oocyte maturation has been investigated.</p> <p>Results</p> <p>Our results demonstrate that CDC2 transiently associates with a single domain, identified as a cluster of endoplasmic reticulum (ER) exit sites (ERES) by the presence of SEC23, in the cortex of maturing porcine oocytes prior to germinal vesicle break down. Inhibition of meiosis resumption by forskolin treatment prevented translocation of CDC2 to this ERES cluster. Phosphorylated GM130 (P-GM130), which is a marker for fragmented Golgi, localized to ERES in almost all immature oocytes and was not affected by forskolin treatment. After removal of forskolin from the culture media, the transient translocation of CDC2 to ERES was accompanied by a transient dispersion of P-GM130 into the ER suggesting a role for CDC2 in redistributing Golgi components that have collapsed into ERES further into the ER during meiosis. Finally, we show that SPDY, rather than cyclin B, colocalizes with CDC2 at ERES, suggesting a role for the CDC2/SPDY complex in regulating the secretory pathway during oocyte maturation.</p> <p>Conclusion</p> <p>Our data demonstrate the presence of a novel structure in the cortex of porcine oocytes that comprises ERES and transiently accumulates CDC2 prior to germinal vesicle breakdown. In addition, we show that SPDY, but not cyclin B, localizes to this ERES cluster together with CDC2.</p

Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage)

The effects of tillage on soil properties, crop productivity, and global greenhouse gas emissions have been discussed in the last decades. Global ecosystem models have limited capacity to simulate the various effects of tillage. With respect to the decomposition of soil organic matter, they either assume a constant increase due to tillage or they ignore the effects of tillage. Hence, they do not allow for analysing the effects of tillage and cannot evaluate, for example, reduced tillage or no tillage (referred to here as “no-till”) practises as mitigation practices for climate change. In this paper, we describe the implementation of tillage-related practices in the global ecosystem model LPJmL. The extended model is evaluated against reported differences between tillage and no-till management on several soil properties. To this end, simulation results are compared with published meta-analyses on tillage effects. In general, the model is able to reproduce observed tillage effects on global, as well as regional, patterns of carbon and water fluxes. However, modelled N fluxes deviate from the literature values and need further study. The addition of the tillage module to LPJmL5 opens up opportunities to assess the impact of agricultural soil management practices under different scenarios with implications for agricultural productivity, carbon sequestration, greenhouse gas emissions, and other environmental indicators

The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage

No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions

Distinct RNA profiles in subpopulations of extracellular vesicles: apoptotic bodies, microvesicles and exosomes

Introduction: In recent years, there has been an exponential increase in the number of studies aiming to understand the biology of exosomes, as well as other extracellular vesicles. However, classification of membrane vesicles and the appropriate protocols for their isolation are still under intense discussion and investigation. When isolating vesicles, it is crucial to use systems that are able to separate them, to avoid cross-contamination. Method: EVs released from three different kinds of cell lines: HMC-1, TF-1 and BV-2 were isolated using two centrifugation-based protocols. In protocol 1, apoptotic bodies were collected at 2,000&#x00D7;g, followed by filtering the supernatant through 0.8 &#x00B5;m pores and pelleting of microvesicles at 12,200&#x00D7;g. In protocol 2, apoptotic bodies and microvesicles were collected together at 16,500&#x00D7;g, followed by filtering of the supernatant through 0.2 &#x00B5;m pores and pelleting of exosomes at 120,000&#x00D7;g. Extracellular vesicles were analyzed by transmission electron microscopy, flow cytometry and the RNA profiles were investigated using a Bioanalyzer&#x00AE;. Results: RNA profiles showed that ribosomal RNA was primary detectable in apoptotic bodies and smaller RNAs without prominent ribosomal RNA peaks in exosomes. In contrast, microvesicles contained little or no RNA except for microvesicles collected from TF-1 cell cultures. The different vesicle pellets showed highly different distribution of size, shape and electron density with typical apoptotic body, microvesicle and exosome characteristics when analyzed by transmission electron microscopy. Flow cytometry revealed the presence of CD63 and CD81 in all vesicles investigated, as well as CD9 except in the TF-1-derived vesicles, as these cells do not express CD9. Conclusions: Our results demonstrate that centrifugation-based protocols are simple and fast systems to distinguish subpopulations of extracellular vesicles. Different vesicles show different RNA profiles and morphological characteristics, but they are indistinguishable using CD63-coated beads for flow cytometry analysis

Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)

Open Access Article; Published online: 12 Jun 2020Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes