Drought risk for agricultural systems in South Africa: Drivers, spatial patterns, and implications for drought risk management.

The regular drought episodes in South Africa highlight the need to reduce drought risk by both policy and local community actions. Environmental and socioeconomic factors in South Africa's agricultural system have been affected by drought in the past, creating cascading pressures on the nation's agro-economic and water supply systems. Therefore, understanding the key drivers of all risk components through a comprehensive risk assessment must be undertaken in order to inform proactive drought risk management. This paper presents, for the first time, a national drought risk assessment for irrigated and rainfed systems, that takes into account the complex interaction between different risk components. We use modeling and remote sensing approaches and involve national experts in selecting vulnerability indicators and providing information on human and natural drivers. Our results show that all municipalities have been affected by drought in the last 30 years. The years 1981-1982, 1992, 2016 and 2018 were marked as the driest years during the study period (1981-2018) compared to the reference period (1986-2015). In general, the irrigated systems are remarkably less often affected by drought than rainfed systems; however, most farmers on irrigated land are smallholders for whom drought impacts can be significant. The drought risk of rainfed agricultural systems is exceptionally high in the north, central and west of the country, while for irrigated systems, there are more separate high-risk hotspots across the country. The vulnerability assessment identified potential entry points for disaster risk reduction at the local municipality level, such as increasing environmental awareness, reducing land degradation and increasing total dam and irrigation capacity.

Land-use changes in Northern Ukraine: patterns and dynamics of illegal amber mining during 1986-2016.

Political instability and economic crises may trigger informal, unsustainable and often illegal land use, leading to land degradation. One of the most recent and striking examples of such a phenomenon within Europe is demonstrated by the Polissia region in Northern Ukraine, famous for its amber deposits. Amid severe political disturbances and subsequent economic recession in Ukraine in 2014 and 2015, amber mining flourished in the region. However, the extent and spatial pattern of degraded land caused by amber mining over the entire region has remained unknown. To fill this gap and track land surface changes, we used multi-source satellite imagery. We found a gradual decrease of the area without vegetation cover, as a proxy for degraded land, from the late Soviet period until 2014 in most of the analysed administrative districts of Polissia. In contrast to this, we identified substantial conversion of forest and agricultural land to bare soil that occurred between 2014 and 2016, which can be attributed to the rush of illegal amber mining in the region. The estimated total area of affected land on the produced Landsat-based map for 2016 was 1066 ha, 60% of which occurred in 2014-2016. Land cover classification within a key study area suggests that utilization of very high-resolution images from the WorldView-2 satellite enables more accurate mapping of land degradation and identification of small mining sites. Further monitoring of land-use change caused by amber mining is essential to improving understanding of long-lasting environmental impacts on regional ecosystems and biodiversity.

HEALTH OF THE TEACHER OF HIGHER EDUCATION INSTITUTIONS (EFFICIENCY - DEVELOPMENT).

OBJECTIVE: The aim: Of the work is to find a scientifically based approach to improve the health of teachers on the basis of a comprehensive socio-hygienic analysis of the factors that affect the state of their health. Identify the main aspects of psychological work with teachers to support the mechanisms of self-regulation of their psychological health. PATIENTS AND METHODS: Materials and methods: Theoretical and methodological analysis of psychological and pedagogical literature; сomparison; generalization; systematization. The article presents the current problem of modernity - the mental health of teachers of higher education. Criteria, quality categories, levels, principles of ensuring the mental health of the teacher as a person are analyzed. The components of mental health are compared. CONCLUSION: Conclusions: The concept of professional psychological health as a process of scientific understanding of the teachers practice involves the development of a comprehensive program for teacher's health care, which will include all areas: informational, preventional, diagnostical, rehabilitational and treatment. Higher education teachers are active participants in the preservation and promotion of health at the state, social and personal levels. They should form the concept of health in students during the process of their professional activities. Teachers use various forms of organizational, educational, volunteer work and different new technologies to preserve their own health and create the environment with the appropriate social conditions, where students can take responsibility for their own actions, deeds, work, leading a healthy lifestyle.

The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations.

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001-2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha-1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.

Development of integrated wetland change detection approach: In case of Erdos Larus Relictus National Nature Reserve, China.

Wetlands are threatened by the global warming and the human exploitation pressure, and have been shrinking quickly in recent years. Timely and accurate wetland area change detection is the primary task for wetland conservation and restoration. The objective of this study is to develop an integrated change detection approach which integrates the advantages of spectral mixture analysis (SMA) and change vector analysis (CVA) for the change identification of wetland dynamics. In the proposed approach, water, vegetation and soil fractions of wetlands were derived by SMA; then, the detailed change information (including change magnitude and 12 change direction categories) were calculated through CVA. The proposed approach was applied for the wetlands change in Erdos Larus Relictus National Nature Reserve (ELRNNR), China, using time-series Landsat images during 1977-2017. We found that the wetland faced serious degradation, with water fraction changed to soil (5.79 km2), to vegetation (1.35 km2) and to both soil and vegetation (3.53 km2). From 1977 to 2000, a slight degradation occurred in the northeast edge of Bojiang Lake and a marginal degradation in Bojiang and Houjia Lakes inside the ELRNNR, with water fraction changed to soil and vegetation. During 2000-2010, severe degradation occurred in ELRNNR, and from 2010 to 2017, the wetland was more susceptible to the precipitation change and human activities. Analysis of the result indicated that the long-term drought and effects of mismanagement as well as misuse by human beings were the driving factors of wetland degradation. The proposed approach in this study achieves a higher accuracy than the classification approach to detect wetland change, with the ability to obtain more detailed change information.

Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective.

Droughts have significant negative impacts on livelihoods and economy of Kazakhstan. In this study, we assessed and characterized drought hazard events in Kazakhstan using satellite Remote Sensing time series for the period between 2000 and 2016. First, we calculated Vegetation Condition Index (VCI) and Standardized Enhanced Vegetation Index anomalies (ZEVI) based on 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series. Second, we assessed vegetation cover changes for the observation period. Third, we analyzed different characteristics of the drought hazard as well as spatial distribution of the drought-affected areas within the country. The results confirmed that drought was one of the environmental challenges for Kazakhstan in 2000-2016. The obtained maps showed that drought hazard conditions were observed every year, though the areal coverage of the drought conditions largely varied between the analyzed years. The calculated drought indices indicated that in years 2000, 2008, 2010, 2011, 2012, and 2014, more than 50% of the area of the country were affected by drought conditions of different severity with the largest droughts in terms of the areal spread occurring in 2012 and 2014. We concluded that the pre-requisite of successful implementation of drought hazard and risk mitigation strategies is availability of spatially explicit, timely, and reliable information on drought hazard. This suggests the necessity of incorporation of remote sensing-based drought information, as was demonstrated in this paper, in the national drought monitoring system of Kazakhstan.

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling.

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000-2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region's area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 %), land-use intensity (odds = 103 %), low soil quality (odds = 49 %), slope (odds = 29 %), and salinity of the groundwater (odds = 26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.