This study depicts an inclusive estimation of climate variation and its effects on agriculture sector in theselected South Asian countries (India, Bangladesh, Pakistan, Nepal, Bhutan and Sri-Lanka) over the period of 1990-2014. Agriculture sector plays vigorous role in the economy of selected South Asian states because more than 60%people work in this sector. The rapid growth of industrialization and weather variation causes the raise of thetemperature level by which reduce production of agriculture crops and the people face heavy losses. Therefore, mainobjective of this study is to detect the influence of the global weather variation in agriculture sector of selected SouthAsian countries. Agriculture sector is used as dependent variable. CO2 emission, gross capital formation, labor forceand temperature are used as explanatory variables. Auto regressive distributed lag model is employed to examine theinfluence of climate variation on the agricultural sector. For analysis panel data were collected from selected SouthAsian countries. The existence of the short and long term relationship between dependent and independent variables isalso assessed by this model. Thus, findings show the climate variation has significant effect on the agricultural sector.In a policy recommendation, government should use sector-wise policies and friendly environmental policies whichminimize the negative effect of climate change.
Given that about 40% of the total food produced globally is lost or wasted, there is an urgent need to understand what, where, why and how much food waste is generated. In this study, we collected the much-needed primary empirical data from the restaurants, hotels and caterers of Lahore, Pakistan through surveys and live tracking/diaries. Specifically, two key performance indicators, waste per customer (g) and percentage waste per day (%), were measured. Waste per customer was found to be 79.9 g (survey) and 73.4 g (live tracking) for restaurants, 138.4 g for hotels and 140.0 g for caterers. Similarly, the percentage of waste per day (%) was found to be 15% (survey) and 17% (live tracking) for restaurants. Results revealed that customer plate leftovers were reported to be the primary source of food waste, followed by inaccurate customer forecasting. Given the food waste levels identified in this study, the development and adoption of a national goal and target aimed at food waste reduction could usefully guide the efforts of all stakeholders. To achieve this, we need to build the capacity of all the relevant stakeholders on food loss and waste measurements and ensure national food waste reporting.
Cotton production is highly vulnerable to climate change, and heat stress is a major constraint in the cotton zone of Punjab, Pakistan. Adaptation is perceived as a critical step to deal with forecasted and unexpected climatic conditions. The objective of this study was to standardize and authenticate a cotton crop model based on climate and crop husbandry data in order to develop an adaptation package for cotton crop production in the wake of climate change. For the study, the data were collected from the cotton-growing areas of Punjab, viz. Bahawalpur and Khanewal. After the calibration and validation against field data, the Cropping System Model CSM–CROPGRO–Cotton in the shell of the decision support system for agro-technology transfer (DSSAT) was run with a future climate generated under two representative concentrations pathways (RCPs), viz. RCPs 4.5 and 8.5 with five global circulation models (GCMs). The whole study showed that a model is an artistic tool for examining the temporal variation in cotton and determining the potential impact of planting dates on crop growth, phenology, and yield. The results showed that the future climate would have drastic effects on cotton production in the project area. Reduction in seed cotton yield (SCY) was 25.7% and 32.2% under RCPs 4.5 and 8.5, respectively. The comparison of five GCMs showed that a hot/wet climate would be more damaging than other scenarios. The simulations with different production options showed that a 10% and 5% increase in nitrogen and plant population, respectively, compared to the present would be the best strategy in the future. The model further suggested that planting conducted 15 days earlier, combined with the use of water and nitrogen (fertigation), would help to improve yield with 10% less water under the future climate. Overall, the proposed adaptation package would help to recover 33% and 37% of damages in SCY due to the climate change scenarios of RCP 4.5 and 8.5, respectively. Furthermore, the proposed package would also help the farmers increase crop yield by 7.5% over baseline (current) yield.
The Synthetic and botanical insecticides are relatively safer for environment and beneficial insects. The study was conducted in Rahim Yar Khan during the cotton cropping season 2014 to evaluate the comparative efficacy of two Synthetic insecticides i.e. Nitenpyram (Jasper 10% SL) and Pyriproxyfen (Bruce 10.8% EC) and two botanical extracts of Calotropic procera and Azadirachta indica, against sucking insect pest complex of cotton and their natural enemies. Upon reaching economic thresholds, the recommended field doses of all the insecticides were applied on cotton cultivar MNH-886. Data against sucking pests and their natural enemies was recorded 24 hours prior to insecticidal application and then 24, 48, 72 and 96 hours after insecticidal application. Results revealed that Nitenpyram was much toxic against sucking pests followed by Pyriproxyfen as compared to two botanical extracts. On the other hand, the synthetic insecticides did not prove safer for natural enemies as compared to botanical extracts. It was concluded that as an Integrated Pest Management (IPM) strategy, botanical extracts can be used at low infestation levels so that ecosystem service of biological control may be sustained.
Better management practices (BMPs) as a sustainable approach made it attractive for growers to control the provision of pollutants from agricultural activities as well as enhance the financial return. The experiments of cotton production were conducted in four different regions of Punjab in cotton-growing years 2017-2019. The objective of the study was to evaluate the potential impact of BMPs among cotton farmers by rationalizing the use of input resources (viz., seed, fertilizers, pesticides and water). The data were collected from randomly selected adopters of BMPs (n = 400) and non-adopters of BMPs (n = 100) through a well-structured pretested questionnaire using a multistage sampling procedure from four different regions of Punjab province. Descriptive analysis was employing an independent two-sample t-test to evaluate the significant effect of BMPs on the utilization of input resources and profitability of cotton production between adopters and non-adopters of BMPs. The results indicated that adopters of BMPs were efficiently used input resources (at p ≤ 0.001 & p ≤ 0.01) and significantly enhanced the average cotton yield (855.09 kg acre-1) in Punjab, while non-adopters of BMPs had a significantly high cost of production by 11% (35,655 PKR acre-1) and output was lower by 15% (751.70 kg acre-1) under conventional farming for cotton cultivation. The economic analysis revealed that the average gross income gained by adopters of BMPs was significantly high by 11% (72,648 PKR acre-1 at p ≤ 0.001) with the maximum net return of 36% (40,785 PKR acre-1 at p ≤ 0.001) as well as a good B:C (1.28) as compared to non-adopters of BMPs. This study provides useful information about the potential impact of BMPs among cotton farmers even without the extra use of inputs. It is concluded that precision in inputs and management practices with lower input costs can significantly improve cotton productivity leading to uplift the farmers’ profit.
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