No abstract

We examined the faunal composition and abundance of phytoseiid mites (Acari: Phytoseiidae) in apple orchards under different pest management systems in Hungary. A total of 30 apple orchards were surveyed, including abandoned and organic orchards and orchards where integrated pest management (IPM) or broad spectrum insecticides (conventional pest management) were applied. A total of 18 phytoseiid species were found in the canopy of apple trees. Species richness was greatest in the organic orchards (mean: 3.3 species/400 leaves) and the least in the conventional orchards (1.4), with IPM (2.1) and abandoned (2.7) orchards showing intermediate values. The phytoseiid community's Rényi diversity displayed a similar pattern. However, the total phytoseiid abundance in the orchards with different pest management systems did not differ, with abundance varying between 1.8 and 2.6 phytoseiids/10 leaves. Amblyseius andersoni, Euseius finlandicus, and Typhlodromus pyri were the three most common species. The relative abundance of A. andersoni increased with the pesticide load of the orchards whereas the relative abundance of E. finlandicus decreased. The abundance of T. pyri did not change in the apple orchards under different pest management strategies; regardless of the type of applied treatment, they only displayed greater abundance in five of the orchards. The remaining 15 phytoseiid species only occurred in small numbers, mostly from the abandoned and organic orchards. We identified a negative correlation between the abundance of T. pyri and the other phytoseiids in the abandoned and organic orchards. However, we did not find any similar link between the abundance of A. andersoni and E. finlandicus.

This paper analyzes South Africa's Free Basic Water Policy, under which households receive a free water allowance equal to the World Health Organization's recommended minimum. I estimate residential water demand, evaluate the welfare effects of free water, and provide optimal price schedules derived from a social planner's problem. I use a data set of monthly metered billing data for 60,000 households for 2002–2009 from a particularly disadvantaged suburb of Pretoria, with rich price variation across 20 different nonlinear tariff schedules. I find that the free allowance acts as a lump‐sum subsidy, without large effects on water consumption. However, it is possible to reallocate the current subsidy to form an optimal tariff without a free allowance, which would increase welfare while leaving the water provider's profit unchanged. This optimal tariff would also reduce the number of households consuming low quantities of water, a desirable policy goal according to the WHO.

Tanulmányunk célja, hogy bemutassa, miben különbözik, és hogyan kapcsolódik össze a társadalom integrációja, illetve dezintegrációja a társadalom rétegződésével, egyenlőtlenségeivel. Ehhez elsőként összefoglaljuk a társadalmi rétegződéskutatásokat az utóbbi néhány évtizedben ért legnagyobb kihívásokat és az azokra adott válaszkísérleteket. Ezután ezeket a kísérleteket a hazai szakirodalom kritikai összefoglalásával helyezzük el magyar társadalmi kontextusban. A társadalmi integráció és dezintegráció fogalmainak definiálása után az integráció és a társadalmi rétegződés egymáshoz való viszonyát-és ezzel összefüggésben integrációs kutatásunk két fő célkitűzését határozzuk meg: a hagyományos rétegződési paradigmához képest új szempontok szerint is azonosítható csoportok, rétegek, osztályok leírását és képzését, valamint azoknak az integrációs/dezintegrációs mechanizmusoknak a feltárását, amelyek magyarázzák az e csoportok közötti különbségeket.

Due to the increasing global demand of food grain, early and reliable information on crop production is important in decision making in agricultural production. Remote sensing (RS)-based forecast models developed from vegetation indices have the potential to give quantitative and timely information on crops for larger regions or even at farm scale. Different vegetation indices are being used for this purpose, however, their efficiency in estimating crop yield certainly needs to be tested. In this study, wheat yield was derived by linear regressing reported yield values against a time series of six different peak-seasons (2013–2018) using the Landsat 8-derived Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). NDVI- and SAVI-based forecasting models were validated based on 2018–2019 datasets and compared to evaluate the most appropriate index that performs better in forecasting wheat production in the Tisza river basin. Nash-Sutcliffe efficiency index was positive with E1 = 0.716 for the model from NDVI and for SAVI E1 = 0.909, which means that the forecasting method developed and performed good forecast efficiency. The best time for wheat yield prediction with Landsat 8-SAVI and NDVI was found to be the beginning of full biomass period from the 138th to 167th day of the year (18 May to 16 June; BBCH scale: 41–71) with high regression coefficients between the vegetation indices and the wheat yield. The RMSE of the NDVI-based prediction model was 0.357 t/ha (NRMSE: 7.33%). The RMSE of the SAVI-based prediction model was 0.191 t/ha (NRMSE 3.86%). The validation of the results revealed that the SAVI-based model provided more accurate forecasts compared to NDVI. Overall, probable yield amount is possible to predict far before harvest (six weeks earlier) based on Landsat 8 NDVI and SAVI and generating simple thresholds for yield forecasting, and a potential loss of wheat yield can be mapped.