A Regression Model to Estimate Regional Ground Water Recharge

A regional regression model was developed to estimate the spatial distribution of ground water recharge in subhumid regions. The regional regression recharge (RRR) model was based on a regression of basin-wide estimates of recharge from surface water drainage basins, precipitation, growing degree days (GDD), and average basin specific yield (SY). Decadal average recharge, precipitation, and GDD were used in the RRR model. The RRR estimates were derived from analysis of stream base flow using a computer program that was based on the Rorabaugh method. As expected, there was a strong correlation between recharge and precipitation. The model was applied to statewide data in Minnesota. Where precipitation was least in the western and northwestern parts of the state (50 to 65 cm/year), recharge computed by the RRR model also was lowest (0 to 5 cm/year). A strong correlation also exists between recharge and SY. SY was least in areas where glacial lake clay occurs, primarily in the northwest part of the state; recharge estimates in these areas were in the 0- to 5-cm/year range. In sand-plain areas where SY is greatest, recharge estimates were in the 15- to 29-cm/year range on the basis of the RRR model. Recharge estimates that were based on the RRR model compared favorably with estimates made on the basis of other methods. The RRR model can be applied in other subhumid regions where region wide data sets of precipitation, streamflow, GDD, and soils data are available

Novel and emerging biotechnological crop protection approaches

Traditional breeding or genetically modified organisms (GMOs) have for a long time been the sole approaches to effectively cope with biotic and abiotic stresses and implement the quality traits of crops. However, emerging diseases as well as unpredictable climate changes affecting agriculture over the entire globe force scientists to find alternative solutions required to quickly overcome seasonal crises. In this review, we first focus on cisgenesis and genome editing as challenging biotechnological approaches for breeding crops more tolerant to biotic and abiotic stresses. In addition, we take into consideration a toolbox of new techniques based on applications of RNA interference and epigenome modifications, which can be adopted for improving plant resilience. Recent advances in these biotechnological applications are mainly reported for non‐model plants and woody crops in particular. Indeed, the characterization of RNAi machinery in plants is fundamental to transform available information into biologically or biotechnologically applicable knowledge. Finally, here we discuss how these innovative and environmentally friendly techniques combined with traditional breeding can sustain a modern agriculture and be of potential contribution to climate change mitigation