Assessing the relative impacts of the factors affecting MIS adoption process

Micro-irrigation systems (MIS) have been at the forefront of policy-making and social research in exploring determinants that could potentially impact the adoption of MIS technologies in the field to fulfil the basic aim of enhanced agricultural productivity and enriched nutritional quality of the produce with optimal adoption of natural resources. Therefore, this study was undertaken to determine why MIS technologies have not been adopted to the extent anticipated, so that suitable policy schemes, promotional schemes and socio-technical frameworks could be formulated for their enhanced adoption to enhance the socio-economic status of the farming community in the Dahod district of Gujarat State, India. A study of 350 non-MIS (NMIS) and 350 MIS farmers was conducted to identify factors affecting the MIS adoption process. The logit model was fitted using XLSTAT software (XLSTAT 2014.1.04) to the explanatory variables (determinants) of the MIS adoption process. Type III analysis and ANOVA were conducted to test the relative significance of the explanatory variables adopted. It was found that total income had the highest weight (or beta coefficient, i.e. 0.625) followed by total land area (0.546), motor horsepower (0.499), dependency ratio (0.397), and education (0.295) and age of household head (0.207). Furthermore, to assess the efficacy of the logit model, the ROC curve was also developed and the AUC was found to be 0.881, and therefore the model was considered to discriminate well in identifying the factors affecting the MIS adoption process. The study found that higher total income and education level increase the likelihood of MIS adoption and agricultural water management, and therefore special training programmes on installation, as well as repair and maintenance, of MIS systems and agricultural water management can be planned at the institutional/organisation level. The total cultivable area is also one of the important determinants in MIS adoption, and therefore the adoption of MIS schemes should not be restricted to large farmers only, but rather should be extended to both small and marginal farmers.     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

Size exclusion chromatography for the removal of pigments from extracellular ligninolytic enzyme extracts from decayed wheat straw

Solid-state fermentation of wheat straw was carried out by a native white rot basidiomycete Daedaleopsis flavida strain 5A. Extract prepared from the 12-day decayed wheat straw contained extracellular ligninolytic enzymes like manganese peroxidase (MnP), manganese-independent peroxidase (MIP), lignin peroxidase (LiP) and laccase along with straw-degraded products and pigments. Sephacryl S-200 size exclusion chromatography in 16/100 column was used for the separation of these ligninolytic enzymes and straw-degraded products and pigments. Recovery of pigment-free ligninolytic enzyme activities as protein was 40% of the total proteins loaded and specific LiP activity increased 34 fold after size exclusion chromatography. Thus accurate estimation of LiP by veratryl alcohol oxidation assay was possible only after the removal of interfering pigments. The reproducibility of size exclusion chromatography is adjudged satisfactory from the consistent results obtained after seven repetitive uses of matrices.     

Removal of arsenate from water by adsorbents: a comparative case study

Laboratory and field filtration experiments were conducted to study the effectiveness of As(V) removal for five types of adsorbent media. The media included activated alumina (AA), modified activated alumina (MAA), granular ferric hydroxide (GFH), granular ferric oxide (GFO), and granular titanium dioxide (TiO₂). In laboratory batch and column experiments, the synthetic challenge water was used to evaluate the effectiveness for five adsorbents. The results of the batch experiments showed that the As(V) adsorption decreased as follows at pH 6.5: TiO₂ > GFO > GFH > MAA > AA. At pH 8.5, however, As(V) removal decreased in the following order: GFO = TiO₂ > GFH > MAA > AA. In column experiments, at pH 6.5, the adsorbed As(V) for adsorbents followed the order: TiO₂ > GFO > GFH, whereas at pH 8.5 the order became: GFO = TiO₂ > GFH when the challenge water containing 50 μg/L of As(V) was used. Field filtration experiments were carried out in parallel at a wellhead in New Jersey. Before the effluent arsenic concentration increased to 10 μg/L, approximately 58,000 and 41,500 bed volumes of groundwater containing an average of 47 μg/L of As(V) were treated by the filter system packed with GFO and TiO₂, respectively. The As(V) adsorption decreased in the following sequence: GFO > TiO₂ > GFH > MAA > AA. Filtration results demonstrated that GFO and TiO₂ adsorbents could be used as media in small community filtration systems for As(V) removal.     

Development of an extraction method for perchlorate in soils

Perchlorate originates as a contaminant in the environment from its use in solid rocket fuels and munitions. The current US EPA methods for perchlorate determination via ion chromatography using conductivity detection do not include recommendations for the extraction of perchlorate from soil. This study evaluated and identified appropriate conditions for the extraction of perchlorate from clay loam, loamy sand, and sandy soils. Based on the results of this evaluation, soils should be extracted in a dry, ground (mortar and pestle) state with Milli-Q water in a 1 ratio 1 soil ratio water ratio and diluted no more than 5-fold before analysis. When sandy soils were extracted in this manner, the calculated method detection limit was 3.5 microg kg(-1). The findings of this study have aided in the establishment of a standardized extraction method for perchlorate in soil.     

Reduction of pollutants in pulp paper mill effluent treated by PCP-degrading bacterial strains

Two PCP-degrading bacterial strains, Bacillus cereus (ITRC-S6) and Serratia marcescens (ITRC-S7) were used for the treatment of pulp and paper mill effluent at conditions; 1.0% glucose and 0.5% peptone at 30 +/- 1 degrees C at 120 rpm for 168 h of incubation. These two bacterial strains effectively reduced colour (45-52%), lignin (30-42%), BOD (40-70%), COD (50-60%), total phenol (32-40%) and PCP (85-90%) within 168 h of incubation. However, the highest reduction in colour (62%), lignin (54%), BOD (70%), COD (90%), total phenol (90%) and PCP (100%) was recorded by mixed culture treatment. The bacterial mechanism for the degradation of pulp and paper mill effluent may be explained by an increase in the cells biomass using added co-substrates resulting liberation of significant amount of chloride due to bacterial dechlorination of chlorolignins and chlorophenols this showed reduction in colour, lignin and toxicity in the effluent. Further, GC-MS analysis of ethyl acetate-extractable compounds from treated pulp paper mill effluent reinforces the bacterium capability for the degradation of lignin and pentachlorophenol, as many aromatic compounds such as 2-chlorophenol, 2, 4, 6-trichlorophenol and tetrachlorohydroquinone, 6-chlorohydroxyquinol and tetrachlorohydroquinone detected which were not present in the untreated effluent.     

Physiological and biochemical responses of several freshwater algae to a mixture of metals

A mixture of ten metals (As, Cd, Cr, Cu, Fe, Pb, Hg, Ni, Se and Zn) at the Great Lakes Water Quality Objective levels was found to reduce the primary productivity of four cultured freshwater algae ($\text{Scenedesmus}$, $\text{Chlorella}$, $\text{Anabaena}$ and $\text{Navicula}$) as well as natural phytoplankton from Lake Ontario water. The metal mixture also caused a reduction in the reproduction and transport of an amino acid analogue (α-aminoisobutyric acid) by $\text{Ankistrodesmus}$. The depuration and exchange of the amino acid analogue were not affected by the metal mixture. The nitrogenase activity of $\text{Anabaena}$ was, however, reduced in the presence of the metal mixture.     

Why bees are critical for achieving sustainable development

Reductions in global bee populations are threatening the pollination benefits to both the planet and people. Whilst the contribution of bee pollination in promoting sustainable development goals through food security and biodiversity is widely acknowledged, a range of other benefits provided by bees has yet to be fully recognised. We explore the contributions of bees towards achieving the United Nation’s Sustainable Development Goals (SDGs). Our insights suggest that bees potentially contribute towards 15 of the 17 SDGs and a minimum of 30 SDG targets. We identify common themes in which bees play an essential role, and suggest that improved understanding of bee contributions to sustainable development is crucial for ensuring viable bee systems.