Methane Emissions from Paddy Rice Fields: Strategies towards Achieving A Win-Win Sustainability Scenario between Rice Production and Methane Emission Reduction - Epule, Epule Terence; Peng, Changhui; Mafany, Ndiva Mongoh (Journal of Sustainable Development, 2011)

Rainfall and Deforestation Dilemma for Cereal Production in the Sudano-Sahel of Cameroon - Epule, Terence Epule; Peng, Changhui; Lepage, Laurent; Chen, Zhi (Journal of Agricultural Science, 2012)

Forest Loss Triggers in Cameroon: A Quantitative Assessment Using Multiple Linear Regression Approach - Epule, Epule Terence; Peng, Changhui; Lepage, Laurent; Chen, Zhi (Journal of Geography and Geology, 2011)

Enabling Conditions for Successful Greening of Public Spaces: The Case of Touroua, Cameroon Based on Perceptions - Epule, Epule Terence; Peng, Changhui; Lepage, Laurent; Chen, Zhi (Small-scale Forestry, 2014)

The causes, effects and challenges of Sahelian droughts: a critical review - Epule, Epule Terence; Peng, Changhui; Lepage, Laurent; Chen, Zhi (Regional Environmental Change, 2014)

Policy options towards deforestation reduction in Cameroon: An analysis based on a systematic approach - Epule, Epule Terence; Peng, Changhui; Lepage, Laurent; Chen, Zhi (Land Use Policy, 2014)

Comparing standardised precipitation and barley yield across Quebec - Epule, Terence Epule; Poirier, Vincent; Lafontaine, Simon; Peng, Changhui (Environmental Science and Pollution Research, 2024)

Well Water Quality and Public Health Implications: the Case of Four Neighbourhoods of the City of Douala Cameroon - Epule, Terence Epule; Peng, Changhui; Mirielle Wase, Moto; Mafany, Ndiva Mongoh (Global Journal of Health Science, 2011)

Identifying maize yield and precipitation gaps in Uganda - Epule, Terence Epule; Dhiba, Driss; Etongo, Daniel; Peng, Changhui; Lepage, Laurent (SN Applied Sciences, 2021)
Abstract In sub-Saharan Africa (SSA), precipitation is an important driver of agricultural production. In Uganda, maize production is essentially rain-fed. However, due to changes in climate, projected maize yield targets have not often been met as actual observed maize yields are often below simulated/projected yields. This outcome has often been attributed to parallel gaps in precipitation. This study aims at identifying maize yield and precipitation gaps in Uganda for the period 1998–2017. Time series historical actual observed maize yield data (hg/ha/year) for the period 1998–2017 were collected from FAOSTAT. Actual observed maize growing season precipitation data were also collected from the climate portal of World Bank Group for the period 1998–2017. The simulated or projected maize yield data and the simulated or projected growing season precipitation data were simulated using a simple linear regression approach. The actual maize yield and actual growing season precipitation data were now compared with the simulated maize yield data and simulated growing season precipitation to establish the yield gaps. The results show that three key periods of maize yield gaps were observed (period one: 1998, period two: 2004–2007 and period three: 2015–2017) with parallel precipitation gaps. However, in the entire series (1998–2017), the years 2008–2009 had no yield gaps yet, precipitation gaps were observed. This implies that precipitation is not the only driver of maize yields in Uganda. In fact, this is supported by a low correlation between precipitation gaps and maize yield gaps of about 6.3%. For a better understanding of cropping systems in SSA, other potential drivers of maize yield gaps in Uganda such as soils, farm inputs, crop pests and diseases, high yielding varieties, literacy, and poverty levels should be considered.

African climate change policy performance index - Epule, Terence Epule; Chehbouni, Abdelghani; Dhiba, Driss; Moto, Mirielle Wase; Peng, Changhui (Environmental and Sustainability Indicators, 2021)

An assessment of the predictors of the dynamics in arable production per capita index, arable production and permanent cropland and forest area based on structural equation models - Epule, Epule Terence; Bryant, Christopher Robin; Akkari, Cherine; Sarr, Mamadou Adama; Peng, Changhui (SpringerPlus, 2014)

Vulnerability of maize, millet, and rice yields to growing season precipitation and socio-economic proxies in Cameroon - Epule, Terence Epule; Chehbouni, Abdelghani; Dhiba, Driss; Etongo, Daniel; Driouech, Fatima; Brouziyne, Youssef; Peng, Changhui (PLOS ONE, 2021)
In sub-Saharan Africa growing season precipitation is affected by climate change. Due to this, in Cameroon, it is uncertain how some crops are vulnerable to growing season precipitation. Here, an assessment of the vulnerability of maize, millet, and rice to growing season precipitation is carried out at a national scale and validated at four sub-national scales/sites. The data collected were historical yield, precipitation, and adaptive capacity data for the period 1961–2019 for the national scale analysis and 1991–2016 for the sub-national scale analysis. The crop yield data were collected for maize, millet, and rice from FAOSTAT and the global yield gap atlas to assess the sensitivity both nationally and sub-nationally. Historical data on mean crop growing season and mean annul precipitation were collected from a collaborative database of UNDP/Oxford University and the climate portal of the World Bank to assess the exposure both nationally and sub-nationally. To assess adaptive capacity, literacy, and poverty rate proxies for both the national and regional scales were collected from KNOEMA and the African Development Bank. These data were analyzed using a vulnerability index that is based on sensitivity, exposure, and adaptive capacity. The national scale results show that millet has the lowest vulnerability index while rice has the highest. An inverse relationship between vulnerability and adaptive capacity is observed. Rice has the lowest adaptive capacity and the highest vulnerability index. Sub-nationally, this work has shown that northern maize is the most vulnerable crop followed by western highland rice. This work underscores the fact that at different scales, crops are differentially vulnerable due to variations in precipitation, temperature, soils, access to farm inputs, exposure to crop pest and variations in literacy and poverty rates. Therefore, caution should be taken when transitioning from one scale to another to avoid generalization. Despite these differences, in the sub-national scale, western highland rice is observed as the second most vulnerable crop, an observation similar to the national scale observation.

Model prediction of biome‐specific global soil respiration from 1960 to 2012 - Zhao, Zhengyong; Peng, Changhui; Yang, Qi; Meng, Fan‐Rui; Song, Xinzhang; Chen, Shutao; Epule, Terence Epule; Li, Peng; Zhu, Qiuan (Earth's Future, 2017)
Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2 = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome