Spatiotemporal Variations in Seed Set and Pollen Limitation in Populations of the Rare Generalist Species Polemonium caeruleum in Poland

A vast majority of angiosperms are pollinated by animals, and a decline in the number and diversity of insects often affects plant reproduction through pollen limitation. This phenomenon may be particularly severe in rare plant species, whose populations are shrinking. Here, we examined the variability in factors shaping reproductive success and pollen limitation in red-listed Polemonium caeruleum L. During a 5-year study in several populations of P. caeruleum (7–15, depending on year), we assessed the degree of pollen limitation based on differences in seed set between open-pollinated (control) and hand-pollinated flowers. We analysed the effects of flower visitors, population size, and meteorological data on plant reproductive success and pollen limitation. Our study showed that pollen limitation rarely affected P. caeruleum populations, and was present mainly in small populations. Pollen limitation index was negatively affected by the size of population, visitation frequency of all insects, and when considering the visitation frequency of individual groups, also by honeybee visits. Seed production in control treatment was positively influenced by the population size, average monthly precipitation in June and visits of hoverflies, while visits of honeybees, average monthly temperature in September, and average monthly precipitation in August influenced seed production negatively. As generalist plant P. caeruleum can be pollinated by diverse insect groups, however, in small populations their main visitors, the honeybees and bumblebees, may be less attracted, eventually leading to the disappearance of these populations. In pollination of P. caeruleum managed honeybees may play a dual role: while they are the most frequent and efficient flower visitors, their presence decreases seed set in open-pollinated flowers, which is most probably related to efficient pollen collection by these insects.

Relating goat milk production according to the Normalized Difference Vegetation Index and precipitations in the Chaco forest throughout the 2016-2018 period

This work aimed to quantify and relate goat milk production and the Normalized Difference of Vegetation Index of the semiarid Chaco forest and the monthly average precipitation along the 2016-2018 period. The work was carried out in El Polear, in Santiago del Estero, Argentina. Even though the NDVI of the forest and its lower strata biomass productivity were affected by drought, its milk production curve remained unaffected. This may be due to the forest stability resulting from the deep rooted trees that includes, to the strategic displacement of the phenophase in its lower strata (broadleaves, herbaceous) in drought seasons and the adaptation to the changes in the goat diet selectivity before forage fluctuations. Winter NDVI peaks should be considered for new lines of research on their contribution to the energetic reserves of the goat component at the beginning of winter. Significant straight relationships (p<0.05) were found between the average goat milk production and the average monthly precipitation (r=0.64) as well as the NDVI and the semiarid Chaco forest (r=0.59). The resulting linear models involving goat milk production with both precipitation and NDVI had moderate and significant (p<0.05) explaining power (R2=0.41) and (R2=0.35), respectively. These models make the seasonal goat milk production predictable and the planning and the making decision process of both producers and the agroindustry easier.

Assessment of Precipitation Variability and Trends Based on Satellite Estimations for a Heterogeneous Colombian Region

Adequate water resources management includes understanding patterns and spatiotemporal variability of precipitation, as this variable is determinant for ecosystems’ stability, food security, and most human activities. Based on satellite estimations validated through ground measurements from 59 meteorological stations, the objective of this study is to evaluate the long-term spatiotemporal variability and trends of the average monthly precipitation in the Magdalena Department, Colombia, for the 1981–2018 period. This heterogeneous region comprises many different ecoregions in its 23,188 km2 area. The analysis of spatial variability allowed for the determination of four different subregions based on the differences in the average values of precipitation and the degree of rainfall variability. The trend analysis indicates that the current rainfall patterns contradict previous estimates of a progressive decrease in annual averages due to climate change in the study region, as most of the department does not exhibit statistically significant trends, except for the Sierra Nevada de Santa Marta area, where this study found reductions between 10 mm yr−1 and 30 mm yr−1. The findings of this study also suggest the existence of some links between precipitation patterns with regional phenomena of climate variability and solar activity.

Neural network method for climate forecasting water content of the Chernorechensk reservoir

New method for precipitation forecasting at the Ai-Petri region is proposed in this work. This method includes a model based on artificial neural networks. A set of global oceanic and meteorological indices were used as the input parameters of the model. SST and SLP data sets from NCEP / NCAR and HadISST re-analyses in 1950-2020 were used for indices calculation. A feature of the proposed model is the decomposition of the predicted series into two orthogonal signals, their independent modeling and subsequent addition of the calculated model values. The sum of model calculations signals was verified. The model was verified in the period 2007–2020. The possibility of forecasting average monthly precipitation amounts with a lead time of up to 6 months is shown. It is found the possibility of the model to predict precipitation in the winter and summer seasons, September and October, which is 70% of the average long-term annual precipitation. It is shown that the best forecast of precipitation for the winter season can be made in November, and with a higher quality of the forecast – in December. The average absolute deviation in the control sample was 28% and 23%, respectively. Taking into account that the maximum precipitation in the mountains occurs in the cold half of the year, and the fact that the main volume of water content of the Chernorechensk reservoir is formed in the cold period of the year, then the forecast of precipitation for the winter season is of the greatest importance. The forecast of precipitation for the summer period and September-October can be made in April (the average absolute deviation is 22%). In addition, the work tested and confirmed the possibility of a climate forecast of atmospheric pressure. The obtained results can be useful for early assessment of the level of filling of the Chernorechensk reservoir.

Precipitation in Iran from 2003 to 2021

Satellite data between 2003 and 2021 have been used for Iran's precipitation. The average monthly precipitation is about 20 mm. The maximum amount of monthly precipitation is observed in 2019. The average is 233 mm. The lowest precipitation years in Iran are 2009, 2019 and 2018, respectively, and the highest precipitation is in 2020, 2007 and 2005, respectively.

Precipitation in Iran from 2003 to 2021

Satellite data between 2003 and 2021 have been used for Iran's precipitation. The average monthly precipitation is about 20 mm. The maximum amount of monthly precipitation is observed in 2019. The average is 233 mm. The lowest precipitation years in Iran are 2009, 2019 and 2018, respectively, and the highest precipitation is in 2020, 2007 and 2005, respectively.

An Assessment of the Filling Process of the Grand Ethiopian Renaissance Dam and Its Impact on the Downstream Countries

The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, has been filling at a fast rate. This project has created issues for the Nile Basin countries of Egypt, Sudan, and Ethiopia. The filling of GERD has an impact on the Nile Basin hydrology and specifically the water storages (lakes/reservoirs) and flow downstream. In this study, through the analysis of multi-source satellite imagery, we study the filling of the GERD reservoir. The time-series generated using Sentinel-1 SAR imagery displays the number of classified water pixels in the dam from early June 2017 to September 2020, indicating a contrasting trend in August and September 2020 for the upstream/downstream water bodies: upstream of the dam rises steeply, while downstream decreases. Our time-series analysis also shows the average monthly precipitation (derived using IMERG) in the Blue Nile Basin in Ethiopia has received an abnormally high amount of rainfall as well as a high amount of runoff (analyzed using GLDAS output). Simultaneously, the study also demonstrates the drying trend downstream at Lake Nasser in Southern Egypt before December 2020. From our results, we estimate that the volume of water at GERD has already increased by 3.584 billion cubic meters, which accounts for about 5.3% of its planned capacity (67.37 billion cubic meters) from 9 July–30 November 2020. Finally, we observed an increasing trend in GRACE anomalies for GERD, whereas, for the Lake Nasser, we observed a decreasing trend. In addition, our study discusses potential interactions between GERD and the rainfall and resulting flood in Sudan. Our study suggests that attention should be drawn to the connection between the GERD filling and potential drought in the downstream countries during the upcoming dry spells in the Blue Nile River Basin. This study provides an open-source technique using Google Earth Engine (GEE) to monitor the changes in water level during the filling of the GERD reservoir. GEE proves to be a powerful as well as an efficient way of analyzing computationally intensive SAR images.

CLIMATIC CONDITIONS OF THE ULYANOVSK REGION AND THEIR CHANGES

The article summarizes the results of changes in climate conditions: the average annual air temperature for 1961-2018 in the Ulyanovsk region increased by 1.8°C, the most significant increase in temperature was observed in the last twenty-five-year period of time, in the winter pe-riod. There is a tendency to increase both average and seasonal values. When analyzing the time values of the dynamics of the average monthly precipitation amounts, it was found that: the min-imum amount of precipitation falls in the winter months, in June – July they are maximum. There is a clear trend of their growth in September, in the winter months: December, January, February, March. A steady decline in precipitation is observed in June, July, August, and November.

The effectiveness of applying herbicides incorn growing technology

Studies conducted on the chernozems of the steppe zone of Ukraine have revealed a direct relationship between thephytosanitary condition of an agricultural ecosystem and the amount of productive moisture used by corn plants from the 0 to 150 cmsoil layer during the period from sowing to harvest. In absolutely cleancrops, corn plants used soil water to the 156 mm depth, while in the plotsfrom which the weeds were not removedsoil water was used to 203 mm and on theherbicide-treated soilto 168–171 mm depth. Full or partial control of weediness of crops makes it possible to save 320–470 m3/ha moisture reserves, which is equal to the average monthly precipitation that fell during the growing season (2018–2019). Effectiveness of the tested crop chemical protection systems was to a large extend determined by the hydrothermal conditions in April - May. High air temperature, lack of agronomically useful rains and dry winds caused drying out of the upper soil layer and decreasedphytotoxicity of pre-emergence herbicides. In this case (2018), the largest percentage of the first wave weeds (89.1 %) was destroyed by a tank mixture of crop protectionagents contained in Frontier Optima (0.8 l/ha + Stellar (0.8 l/ha) + Metolat (0.8 l/ha). In favorable weather, to ensure manifestation of toxicological properties of soil herbicides (2019), the following combined technological schemes for harmful species control in the first stages of corn organogenesis had an advantage: Dual Gold (1.5 l/ha) (before sowing), Stellar (1.25 l/ha) + Metolat (1.25 l/ha) (after emergence of seedlings) and Acris(3 l/ha) (before sowing), Kelvin Plus (0.35 kg/ha) + Hasten (1 l/ha) (after emergence of seedlings). The technical efficiency of the herbicide application reached 98–100 %. In 2018, the highest yield of corn grain (7.85 t/ha) in the protected plots was obtained after application (in the phase of development of 3–5 corn leaves) of the following tank mixture: Frontier Optima (0.8 l/ha) + Stellar (0.8 l/ha) + Metolat (0.8 l/ha). This combination turned out to be the most effective in terms of cost effectiveness of grain production, providing a profitability of 213 %. In 2019, we managed to increase the yields by combining soil and crop protection herbicides. Given the cost and consumption rate of the herbicides used per 1 ha, a combination of synthetic agrochemicals Avatar (2.5 l/ha) (before sowing) + Prima (0.5 l/ha) (after emergence of seedlings) was the best in terms of the main product cost (1579 UAH/t) and profitability (134 %). Key words: weeds, herbicidal system, technical efficiency, productivity, profitability of production.

Pre-disaster planning and preparedness: drought and flood forecasting and analysis in Zimbabwe

A situational analysis of future drought and flood impacts in Zimbabwe is outlined in this present study. The assessment under different scenarios is carried out using Aqueduct Global Flood Analyzer in which all the analyses are based on the gross domestic product (GDP), population, and the present and future (2030) urban damage. In this study, to effectively estimate future changes, three scenarios were employed, namely, IPCC Scenario A2, the Representative Concentration Pathways (RCP) scenario which represents climate change, and the Shared Socio-economic Pathways (SSP) scenario which represents socio-economic change. All these scenarios were employed from the Intergovernmental Panel on Climate Change 5th Assessment Report. To determine current mean monthly precipitation, 1981–2010 data were used and Meteonorm V7 software was employed for the generation of the mean monthly precipitation from 2011 to 2100. The level of flood protection employed is a 10-year one which is used to identify the population at risk, the effects of this event on the GDP and to determine the rate at which urban damage is happening. Utilizing Meteonorm V7 software, average monthly precipitation is predicted. This study determined that, in any year, the majority of Zimbabwe has a low to medium (2–3% probability) flood occurrence in which a 10-year flood has a 10% occurrence probability in any given year. If there is no flood protection employed, this 10-year flood could cause around 74.9 million USD affected GDP, 119 thousand affected population and 49.5 million USD urban damage. As much as it is impossible to eliminate drought and flood events, a diminution approach and proper planning and preparation before their occurrence reduce the economic and social losses.