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Risk assessment relies on cost–benefit analysis by prey species in relation to predator behavior. The lunar cycle can have strong influences on the way that predator and prey species behave in their environment as light levels can influence activity patterns. By examining how lunar phase influences behavior, we may be able to better predict instances of human–wildlife conflict. As part of the Elephants and Sustainable Agriculture in Kenya project, over 5 years of study have been dedicated to mitigating human–elephant conflict in the Kasigau Wildlife Corridor in southern Kenya, focusing on the construction of deterrent fences and the collection of environmental data in and bordering the Rukinga Wildlife Sanctuary. Elephant presence around crop fields was hypothesized to have a negative relationship with lunar light levels because of an increase in perceived risk from typically greater human activity with moonlight, and a positive relationship with increased darkness during the waning moon phases. The lunar phase data were compared to elephant presence within 12 m of experimental crop fields along the boundary of their wild habitat and human agricultural land to provide insight for potential crop raiding events. Elephants were present significantly less during the full and gibbous moon phases when compared to the new moon phase, and significantly more during the new and waning moon phases. Our findings are being shared with the farmers living in the KWC and may be useful to others living in high HEC areas by providing a form of early warning system that also may have broader applications in the mitigation of other human–wildlife interactions. HIGHLIGHTS Dynamic landscape of fear provides novel insight on elephant behavior near agricultural lands with applications for conservation strategies. [ABSTRACT FROM AUTHOR]

The production of crops using hydroponic methods are becoming popular due to their high yield and ability to overcome the challenges facing soil farming including soil degradation, limitation of water access, reduction of farmland and climate change. These systems rely on the quality of the nutrient solution which is directly made accessible at the plant roots. Farmers often lack the information to prevent nutrient deficiencies, until they are visible signs on plants, for example, the yellowing of leaves. Monitoring nutrient solution parameters is one way to prevent nutrient deficiencies before they affect plants negatively. However, obtaining relevant information involves complicated analytical test, whose equipment is not available at the farm level. This article presents the implementation of a neural network based in the Arduino Mega microcontroller to predict pH and EC in a Nutrient Film Technique (NFT) hydroponic system. These predictions of pH and EC levels based on environmental parameters that affect growing plants, such as greenhouse air temperature and humidity, enable farmers to take preventive actions to prevent nutrient deficiencies. Data collection was carried out in a NFT hydroponic system specially designed to reduce energy consumption. The Nonlinear AutoRegressive with eXogenous inputs (NARX) model was developed and trained using Levenberg-Marquardt on MATLAB. The linear regression R showing the relationship between inputs and outputs was 99%. The Mean Square Error (MSE) was 0.0026828 and there were no signs of over or under fitting. The developed neural network model was implemented in the Arduino controller and tested in the field to predict pH and EC values. The results showed that EC values were well predicted compared to pH values. However, the pH values predicted showed small deviations of 0.1 compared to the actual values measure by the sensor. [ABSTRACT FROM AUTHOR]

The combined chilli + metal strip and the stand-alone metal strip fence demonstrated significant deterrent effects when compared to the no deterrent control ( I p i = 0.013 & I p i = 0.019, respectively); this also was true at <=6 m (Table 1). We hypothesised that the combined chilli + metal strip fence would be the most effective at deterring elephants because of the double barriers and the multiple modalities stimulated (visual, olfactory, auditory, gustatory), ensuring that elephants retained a negative association with the fence. Chilli + metal strip fence: a combined lower metal strip fence and upper chilli pepper fence. [Extracted from the article]

The issue of global warming and climate change is strongly receiving public attention and has become a major environmental concern both nationally and internationally. Brewing industries are among the largest consumers of water and the largest source of organic effluent mostly from the brewing, cleaning, and cooling processes which must be treated to allowable levels to reduce environmental pollution. Close to 10 L water is used for every 1 L beer that is brewed, though the amount of water in the final beer is small. A study was undertaken to characterize and assess the variations in the quality of untreated brewery waste water. Samples from different process streams including brewing line, clean in place line and mixing line from two brewing industries in Kenya were analyzed for BOD5, COD, TDS, TSS, sodium, total nitrogen and phosphorous using standard method as per American Public Health Association (APHA). There was a significant variation (p<0.001) in the all the physicochemical parameters between the industries and a significant interaction (p<0.001) between sampling point and the company. Analysis of the BOD to COD ratio showed the biodegradability index to range from 0.039 to 0.567 for brewing line, 0.177 to 0.766 for cleaning in place and 0.776 to 0.911 for mixing point, thus the waste water was found to be easily biodegradable at the mixing point for all the industries. However pretreatment would be required to improve anaerobic digestion. [ABSTRACT FROM AUTHOR]