Wastewater usage in urban and peri-urban agricultural production systems: scenarios from India

The role urban and peri-urban agriculture (UPA) plays in reducing urban poverty and ensuring environmental sustainability was recognized by the Millennium Development Goals (MGDs). India is the world’s largest democratic nation with a population of 1.2 billion. The rapid urbanization and high proportion of people below the poverty line along with higher migration to urban areas make India vulnerable to food crisis and urbanization of poverty. Ensuring jobs and food security among urban poor is a major challenge in India. The role of UPA can be well explained and understood in this context. This paper focuses on the current situation of UPA production in India with special attention to wastewater irrigation. This question is being posed about the various human health risks from wastewater irrigation which are faced by farmers and labourers, and, secondly by consumers. The possible health hazards involve microbial pathogens as well as helminth (intestinal parasites). Based on primary and secondary data, this paper attempts to confirm that UPA is one of the best options to address increasing urban food demand and can serve to complement rural supply chains and reduce ecological food prints in India. “Good practice urban and peri-urban agriculture” necessitates an integrated approach with suitable risk reduction mechanisms to improve the efficiency and safety of UPA production.

Sustainability of Small Scale Farming in a Mountain Region: Case Study of the Khaling Rai Population of the Solukhumbu, Nepal

The Khaling Rai live in a remote area of the mountain region of Nepal. Subsistence farming is central to their livelihood strategy, the sustainability of which was examined in this study. The sustainable livelihood approach was identified as a suitable theoretical framework to analyse the assets of the Khaling Rai. A baseline study was conducted using indicators to assess the outcome of the livelihood strategies under the three pillars of sustainability – economic, social and environmental. Relationships between key factors were analysed. The outcome showed that farming fulfils their basic need of food security, with self-sufficiency in terms of seeds, organic fertilisers and tools. Agriculture is almost totally non-monitized: crops are grown mainly for household consumption. However, the crux faced by the Khaling Rai community is the need to develop high value cash crops in order to improve their livelihoods while at the same time maintaining food security. Institutional support in this regard was found to be lacking. At the same time there is declining soil fertility and an expanding population, which results in smaller land holdings. The capacity to absorb risk is inhibited by the small size of the resource base and access only to small local markets. A two-pronged approach is recommended. Firstly, the formation of agricultural cooperative associations in the area. Secondly, through them the selection of key personnel to be put forward for training in the adoption of improved low-cost technologies for staple crops and in the introduction of appropriate new cash crops.

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R2 (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R2 (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available.

A Survey of the Relationship between Climatic Heat Stress Indices and Fundamental Milk Components Considering Uncertainty

The main purpose of this study is to assess the relationship between four bioclimatic indices for cattle (environmental stress, heat load, modified heat load, and respiratory rate predictor indices) and three main milk components (fat, protein, and milk yield) considering uncertainty. The climate parameters used to calculate the climate indices were taken from the NASA-Modern Era Retrospective-Analysis for Research and Applications (NASA-MERRA) reanalysis from 2002 to 2010. Cow milk data were considered for the same period from April to September when the cows use the natural pasture. The study is based on a linear regression analysis using correlations as a summarizing diagnostic. Bootstrapping is used to represent uncertainty information in the confidence intervals. The main results identify an interesting relationship between the milk compounds and climate indices under all climate conditions. During spring, there are reasonably high correlations between the fat and protein concentrations vs. the climate indices, whereas there are insignificant dependencies between the milk yield and climate indices. During summer, the correlation between the fat and protein concentrations with the climate indices decreased in comparison with the spring results, whereas the correlation for the milk yield increased. This methodology is suggested for studies investigating the impacts of climate variability/change on food and agriculture using short term data considering uncertainty.