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The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.

Total factor productivity (TFP) change is an important driver of long-run economic growth in the construction sector. However, examining TFP alone is insufficient to identify the cause of TFP changes. Therefore, this paper employs the infrequently used Geometric Young Index (GYI) and stochastic frontier analysis (SFA) to measure and decompose the TFP Index (TFPI) at the firm-level from 2009 to 2018 based on Malaysian construction firms' data.

To improve the TFPI estimation, normally unobserved environmental variables were included in the GYI-TFPI model. These are the physical operation of the firm (inland versus marine operation) and regional locality (West Malaysia versus East Malaysia). Consequently, the complete components of TFPI (i.e. technological, environmental, managerial, and statistical noise) can be accurately decomposed.

The results reveal that TFP change is affected by technological stagnation and improvements in technical efficiency but a decline in scale-mix efficiency. Moreover, the effect of environmental efficiency on TFP is most profound. In this case, being a marine construction firm and operating in East Malaysia can reduce TFPI by up to 38%. The result, therefore, indicates the need for progressive policies to improve long-term productivity.

Monitoring and evaluating productivity change allows an informed decision to be made by managers/policy makers to improve firms' competitiveness. Incentives and policies to improve innovation, competition, training, removing unnecessary taxes and regulation on outputs (inputs) could enhance the technological, technical and scale-mix of resources. Furthermore, improving public infrastructure, particularly in East Malaysia could improve regionality locality in relation to the environmental index.

This study contributes to knowledge by demonstrating how TFP components can be completely modelled using an aggregator index with good axiomatic properties and SFA. In addition, this paper is the first to apply and include the GYI and environmental variables in modelling construction productivity, which is of crucial importance in formulating appropriate policies.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

The paper aimed to examine the determinants of agricultural commercialisation of farmers and measured its commercialisation level in the highland of Vietnam.

A sample size of 360 was randomly chosen from a total population of 5,771 farmers, and a structured questionnaire was developed to collect data. Descriptive and inferential statistics, including linear regression analysis, were used to analyse the data.

The descriptive statistics showed that the average commercialisation level of farmers was 56.3%. The regression model result indicated that number of off-farm income sources, farmer's risk perception, farming practices, number of agricultural activities, motorbikes value, ethnicity, distance from the city centre, number of customers, non-traded inputs value, participation in training programmes, family size, farm size, mobile phones value, traded inputs value, land tenure, distance from the local market and education of household head significantly affected agricultural commercialisation of farmers.

Any development strategies that assist farmers in adopting commercial farming in the highland of developing countries should consider these determinants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2022-0161

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

Heavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.

The three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.

GSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.

A workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

Ghana's economy is largely agrarian, and the business of agriculture is dominated by smallholder farmers who are predominantly rural dwellers. As a result, efforts to lift rural farming households from poverty have been narrowed to the promotion of agricultural development to the neglect of the rural non-farm sector. However, this is fast changing in the advent of a burgeoning rural nonfarm economy and must engage the attention of policy actors. This study thus assesses the effect of non-farm participation on households' level of commercialization of agricultural crops in Ghana.

The study applies a generalized structural equation model (GSEM) to the Ghana Living Standards Survey round 6 dataset, a stratified and nationally representative random sample of 16,772 households in 1,200 enumeration areas.

This study finds that non-farm participation increases the produce sold to output ratio. It is concluded that non-farm engagement by farmers boosts commercialization in Ghana. Thus, for the Ghanaian and similar contexts, agricultural development interventions that incorporate non-farm activities are more likely to be successful in improving livelihoods.

The study uses only the ratio of sales value to output value definition for commercialization and acknowledges use of multiple definitions could be superior.

Various empirical studies have examined the link between the farm and nonfarm sectors. This paper is original in its approach as it tackles an aspect of the subject that has been understudied, namely, an exploration of nonfarm and farm linkages from the perspective of agricultural commercialization.