The pot experiment was conducted to access the soil microorganisms biomass (physiological method – Substrate Induced Respiration) and emissions of N2O, CO2 and CH4 (photoacoustic infrared detection method), and grasses biomass (weight method). The obtained results of analysed gases were converted to CO2 equivalent. There was no effect of the microorganisms biomass on the N2O emissions. The increase in CO2 emissions was accompanied by an increase in the microorganisms biomass (r = 0.48) under the conditions of the I swath and acid soil reaction, as well as the II swath and neutral reaction ( r = 0.94). On the other hand, in the case of CH4 emission, such a relationship was noted both swaths under the conditions of neutral reaction ( r = 0.51), but a negative correlation ( r = –0.71) was noted for the acid reaction only at the II swath. The increase in the grasses biomass with the increase in the microorganisms biomass was recorded only at the II swath in neutral reaction ( r = 0.91). In a short period of time, with the neutral soil reaction with the increase in the soil microorganisms biomass, an increase in CO2 sequestration and biomass of cultivated grasses was noted. Information on the determination of the microorganisms groups responsible mainly for the transformation of carbon compounds and CO2 and CH4 emissions from the soils of grasslands would be valuable scientifically.

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann–Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3) ₁₂ model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3) ₁₂, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.

Soil erosion has been severely affecting soil and water resources in semi-arid areas like the Mediterranean. In Morocco, this natural process is accelerated by anthropogenic activities, such as unsustainable soil management, overgrazing, and deforestation. With a drainage area of 395,600 ha, the Bouregreg River Watershed extends from the Middle Atlas Range (Jebel Mtourzgane) to the Sidi Mohamed Ben Abdellah (SMBA) dam reservoir south-east of Rabat. Its contrasted eco-geomorphological landscapes make it susceptible to unprecedented soil erosion due to climate change. Resulting changes in erosive dynamics led to huge amounts of solid loads transported to the catchment outlet and, thus, jeopardised the SMBA dam lifespan due to siltation.
The research aims to quantify the average annual soil losses in this watershed using the Revised Universal Equation of Soil Losses (RUSLE) within a GIS environment. To highlight shifts in land use/land cover patterns and their effects on erosional severity, we have resorted to remote sensing through two Landsat 8 satellite images captured in 2004 and 2019. The C factor was combined with readily available local data regarding major erosion factors, e.g. rainfall aggressiveness ( R), soil erodibility ( K), topography ( LS), and conservation practices ( P). The helped to map the erosion hazard and determine erosion prone areas within the watershed where appropriate water and conservation measures are to be considered. Accordingly, from 2004 to 2019, average annual soil losses increased from 11.78 to 18.38 t∙ha ^–1∙y ^–1, as the watershed area affected by strong erosion (>30 t∙ha ^–1∙y ^–1) evolved from 13.57 to 39.39%.