scholarly journals The impact of climate smart agriculture on household welfare in smallholder integrated crop–livestock farming systems: evidence from Zimbabwe

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Angeline Mujeyi ◽  
Maxwell Mudhara ◽  
Munyaradzi Mutenje
Keyword(s):  
Food Security ◽  
Treatment Effects ◽  
Positive Impact ◽  
Arable Land ◽  
Household Head ◽  
Full Potential ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
Smart Agriculture ◽  
The Impact

Abstract Background Agriculture contributes significantly to the welfare of smallholder farmers, but it has become highly susceptible to climate change, due to its reliance on the increasingly erratic rainfall patterns. Climate Smart Agriculture (CSA) offers important opportunities for enhancing food security and incomes through increased agriculture productivity. Technology evaluation through impact studies provides information on the effect of CSA on farmer welfare, thereby highlighting its potential in optimizing agriculture productivity. This paper analyses the impact of CSA adoption on food security and income of households, using cross-sectional survey data collected from 386 households across four districts in Zimbabwe. The analysis was done using the endogenous switching regression model which controls for selection bias and unobserved heterogeneity, a commonly used method in adoption impact analysis. Results The study found several agricultural and socio-economic factors which affect adoption and food security. The econometric results show that the status of soil fertility in fields, distance to input and output markets, ownership of communication assets, and Total Livestock Units (TLU) have a significant impact on the decision of farmers to adopt CSA. The Average Treatment Effects on the Treated (ATT) and Average Treatment Effects on the Untreated (ATU) were found to be positive and significant for adopters and non-adopters, indicating that CSA adoption has had a significantly positive impact on the welfare of the farmers. An analysis of the outcomes revealed that the characteristics of farmers and farms, as well as market factors, significantly affect the welfare of households. The household income, with reference to the adoption of CSA, was significantly affected by factors such as the education of household head, labour size, TLU, and asset index. Food security was influenced by factors such as the education of household head, TLU, access to sanitation, and arable land size. Conclusions The study concludes by giving policy recommendations centred on the access to inputs, sanitation, and encouraging investing in assets and TLU. The findings indicate that the adoption of CSA has a positive impact on the welfare of farmers. To exploit the full potential of these technologies, the study suggests that access to timely weather forecasts must be ensured, that sanitation must be promoted, and that incentives must be provided for agricultural input agro-dealers to decentralize to rural areas.

The Effect of Extension Services and Credit on Agricultural Production in Bolivia, Peru, and Colombia

2021 ◽  
Author(s):  
Mateus C. R. Neves ◽  
Felipe De Figueiredo Silva ◽  
Carlos Otávio Freitas
Keyword(s):  
Travel Time ◽  
Treatment Effect ◽  
Agricultural Production ◽  
Treatment Effects ◽  
Geographic Information ◽  
Average Treatment Effect ◽  
Extension Services ◽  
Average Treatment ◽  
Andean Countries ◽  
Average Treatment Effects

In this paper we estimate the average treatment effect from access to extension services and credit on agricultural production in selected Andean countries (Bolivia, Peru, and Colombia). More specifically, we want to identify the effect of accessibility, here represented as travel time to the nearest area with 1,500 or more inhabitants per square kilometer or at least 50,000 inhabitants, on the likelihood of accessing extension and credit. To estimate the treatment effect and identify the effect of accessibility on these variables, we use data from the Colombian and Bolivian Agricultural Censuses of 2013 and 2014, respectively; a national agricultural survey from 2017 for Peru; and geographic information on travel time. We find that the average treatment effect for extension is higher compared to that of credit for farms in Bolivia and Peru, and lower for Colombia. The average treatment effects of extension and credit for Peruvian farms are $2,387.45 and $3,583.42 respectively. The average treatment effect for extension and credit are $941.92 and $668.69, respectively, while in Colombia are $1,365.98 and $1,192.51, respectively. We also find that accessibility and the likelihood of accessing these services are nonlinearly related. Results indicate that higher likelihood is associated with lower travel time, especially in the analysis of credit.

scholarly journals Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Subgroup Analysis

Healthcare ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Lorraine Johnson ◽  
Mira Shapiro ◽  
Jennifer Mankoff
Keyword(s):  
Lyme Disease ◽  
Treatment Effects ◽  
Group Analysis ◽  
Treatment Protocol ◽  
Medical Community ◽  
Patient Registries ◽  
Average Treatment ◽  
Individualized Care ◽  
Patient Reported ◽  
Average Treatment Effects

Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that sub-group analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.

scholarly journals Estimation of Population Average Treatment Effects in the FIRST Trial: Application of a Propensity Score-Based Stratification Approach

2017 ◽  
Vol 53 (4) ◽  
pp. 2567-2590 ◽  
Author(s):  
Jeanette W. Chung ◽  
Karl Y. Bilimoria ◽  
Jonah J. Stulberg ◽  
Christopher M. Quinn ◽  
Larry V. Hedges
Keyword(s):  
Propensity Score ◽  
Treatment Effects ◽  
Average Treatment ◽  
Population Average ◽  
Average Treatment Effects

scholarly journals Assessing the Potential Contribution of Pfumvudza Towards Climate Smart Agriculture in Zimbabwe: A Review

2021 ◽  
Author(s):  
Never Mujere
Keyword(s):  
Climate Change ◽  
Nitrous Oxide ◽  
Food Security ◽  
Nitrous Oxide Emissions ◽  
Household Food Security ◽  
Potential Contribution ◽  
Mitigation And Adaptation ◽  
Household Food ◽  
Smart Agriculture ◽  
The Impact

Concerns of food and environmental security have increased enormously in recent years due to the vagaries of climate change and variability. Efforts to promote food security and environmental sustainability often reinforce each other and enable farmers to adapt to and mitigate the impact of climate change and other stresses. Some of these efforts are based on appropriate technologies and practices that restore natural ecosystems and improve the resilience of farming systems, thus enhancing food security. Climate smart agriculture (CSA) principles, for example, translate into a number of locally-devised and applied practices that work simultaneously through contextualised crop-soil-water-nutrient-pest-ecosystem management at a variety of scales. The purpose of this paper is to review concisely the current state-of-the-art literature and ascertain the potential of the Pfumvudza concept to enhance household food security, climate change mitigation and adaptation as it is promoted in Zimbabwe. The study relied heavily on data from print and electronic media. Datasets pertaining to carbon, nitrous oxide and methane storage in soils and crop yield under zero tillage and conventional tillage were compiled. Findings show that, compared to conventional farming, Pfumvudza has great potential to contribute towards household food security and reducing carbon emissions if implemented following the stipulated recommendations. These include among others, adequate land preparation and timely planting and acquiring inputs. However, nitrous oxide emissions tend to increase with reduced tillage and, the use of artificial fertilizers, pesticides and herbicides is environmentally unfriendly.

scholarly journals DAMPAK PERUBAHAN PENGGUNAAN LAHAN PERTANIAN TERHADAP SOSIAL EKONOMI PETANI SEKITAR LOKASI PERTAMBANGAN BANYU URIP KECAMATAN GAYAM KABUPATEN BOJONEGORO

2018 ◽  
Vol 20 (1) ◽  
pp. 50
Author(s):  
Aziz Bahtiar Rifa’i ◽  
Fadjar Hari Mardiansjah
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Agricultural Land ◽  
Positive Impact ◽  
Arable Land ◽  
Mining Area ◽  
Agricultural Land Use ◽  
Socioeconomic Conditions ◽  
Qualitative Approaches ◽  
The Impact

This study examines the impact of agricultural land use change into a petroleum mining area to farmers’ socioeconomic conditions around petroleum mining project area in Gayam District of Kabupaten Bojonegoro. The analyses used a mix method, using both quantitative and qualitative approaches. The quantitative approach is used to determine the impact of agricultural land use change on the social and economic aspects with the respondent farmers were supported with a scoring method to determine the condition of socioeconomic vulnerability of farmers, while the qualitative approach carried out through in-depth interviews to some informants who have been affected by the project. The results shows that the presence of the petroleum mining industry has not had a positive impact yet on the farmers’ socioeconomic conditions. By the 700 hectares of agricultural land conversion, the agricultural production capacity of the area tends to decrease, including by the decreasing of the productivity of some agricultural land in a radius of 500 m from the fenceof the mining area as they are affected by the fence’s spotlight. The farmers' income also tends to decrease because of the decreasing of their working hours as the big loss of agricultural land in the area. As a result, many farmers should work outside of the area to search replacement of the arable land. These situations lead to a moderate condition of social and economic vulnerability for the farmers, especially for those who still have sufficient assets to meet the needs of their economic. 

scholarly journals Estimation of Personalized Heterogeneous Treatment Effects Using Concatenation and Augmentation of Feature Vectors

2020 ◽  
Vol 29 (05) ◽  
pp. 2050005
Author(s):  
Lev V. Utkin ◽  
Mikhail V. Kots ◽  
Viacheslav S. Chukanov ◽  
Andrei V. Konstantinov ◽  
Anna A. Meldo
Keyword(s):  
Treatment Effects ◽  
Regression Function ◽  
Simple Modification ◽  
Conditional Average ◽  
Treatment Groups ◽  
Feature Vectors ◽  
Average Treatment ◽  
Average Treatment Effects ◽  
The Difference ◽  
New Feature

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions.

scholarly journals Estimating Conditional Average Treatment Effects

2015 ◽  
Vol 33 (4) ◽  
pp. 485-505 ◽  
Author(s):  
Jason Abrevaya ◽  
Yu-Chin Hsu ◽  
Robert P. Lieli
Keyword(s):  
Treatment Effects ◽  
Conditional Average ◽  
Average Treatment ◽  
Average Treatment Effects
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