Research graph
References from Climate change, vegetation dynamics, and maize production in Mozambique: evidence from method of moments quantile regression and machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Modelling the impacts of climate change on cereal crop production in East Africa: evidence from heterogeneous panel cointegration analysis
10.1007/s11356-022-24773-0 · 2023 · External reference
Exploring the heterogeneous effects of agricultural energy consumption and land use on sweet potato productivity in Africa: an application of method of moments quantile regression
10.1016/j.dsef.2025.100093 · External reference
Pathways to sustainable agriculture in Africa: the role of natural resources, renewable energy, and technological innovation
10.1177/0958305x251388945 · External reference
Modeling the dynamic nexus between climate change, ICT, and agriculture in sub-Saharan Africa: a panel quantile ARDL
10.1007/s10708-024-11186-4 · 2024 · External reference
Asymmetric impact of urbanization on food security in Africa: exploring the moderating role of information and communication technology using a panel quantile ARDL-PMG approach
10.1111/grow.70100 · 2026 · External reference
Assessing the impact of agricultural greenhouse gas emissions, financial development, and land use on cereal production in sub-Saharan Africa: an econometric and machine learning approach
10.1007/s10668-026-07786-4 · 2026 · External reference
The impact of renewable energy, internet use and foreign direct investment on carbon dioxide emissions: a method of moments quantile analysis
10.1016/j.renene.2022.03.017 · 2022 · External reference
Agricultural decision system based on advanced machine learning models for yield prediction: case of east African countries
10.1016/j.atech.2022.100048 · 2022 · External reference
Does the use of information and communication technologies improve cereal production in sub-Saharan Africa? A method of moments quantile regression approach
10.1111/cjag.70003 · 2025 · External reference
On the use of NDVI to estimate LAI in field crops: implementing a conversion equation library
10.3390/rs14153554 · 2022 · External reference
Dry spell analysis and maize yields for two semi-arid locations in East Africa
10.1016/s0168-1923(03)00037-6 · 2003 · External reference
The role of climatic changes and financial development to the ASEAN agricultural output: a novel long-run evidence for sustainable production
10.1007/s11356-022-23144-z · 2023 · External reference
Sustainable maize production and climatic change in Nepal: robust role of climatic and non-climatic factors in the long-run and short-run
10.1007/s10668-022-02111-1 · 2023 · External reference
Modeling the climate change impacts on major fruits production: recent evidence from Pakistan
10.1016/j.scienta.2023.112618 · 2024 · External reference
An introduction to machine learning for panel data
10.1007/s11294-021-09815-6 · 2021 · External reference
The measurement and influencing factors of high-quality economic development in China
10.3390/su14159293 · 2022 · External reference
Influence of green ICT and socioeconomic factors on sustainable development: evidence from Chinese provinces
10.1016/j.ribaf.2024.102624 · 2025 · External reference
How can African agriculture adapt to climate change? A counterfactual analysis from Ethiopia
10.3368/le.89.4.743 · 2013 · External reference
Weather extremes shock maize production: current approaches and future research directions in Africa
10.3390/plants13121585 · 2024 · External reference
Testing for granger non-causality in heterogeneous panels
10.1016/j.econmod.2012.02.014 · 2012 · External reference
Assessing the combined effects of temperature, precipitation, total ecological footprint, and carbon footprint on rice production in Nigeria: a dynamic ARDL simulations approach
10.1007/s11356-022-21656-2 · 2022 · External reference
Unresolved reference
2022 · External reference
Time-series NDVI and greenness spectral indices in mid-to-late growth stages enhance maize yield estimation
10.1016/j.fcr.2025.110069 · 2025 · External reference
Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: determinants of adoption in eastern and southern Africa
10.1007/s10584-015-1459-2 · 2015 · External reference
Unresolved reference
2009 · External reference
High-resolution maize yield mapping across Africa using earth observation and machine learning, deep learning, and foundation model
10.1016/j.srs.2025.100344 · 2026 · External reference
Green energy transition in OECD region through the lens of economic complexity and environmental technology: a method of moments quantile regression perspective
10.1016/j.apenergy.2024.123235 · 2024 · External reference
Unresolved reference
2022 · External reference
Prediction of crop yield using Phenological information extracted from remote sensing vegetation index
10.3390/s21041406 · 2021 · External reference
10.1016/s0304-4076(98)00023-2 · 1999 · Admitted local publication
Integrating climate data and remote sensing for maize and wheat yield modelling in Ethiopia’s key agricultural region
10.3390/rs17030491 · 2025 · External reference
Long-term croplands water productivity in response to management and climate in the Western US corn belt
10.1016/j.agwat.2023.108640 · 2024 · External reference
Regression quantiles
10.2307/1913643 · 1978 · External reference
Data-driven machine learning in environmental pollution: gains and problems
10.1021/acs.est.1c06157 · 2022 · External reference
Climate change and food security in EAC region: a panel data analysis
10.1108/reps-12-2018-0039 · 2019 · External reference
Does corruption control enhance ESG-induced firm value? Insights from machine learning analysis
10.1016/j.frl.2024.106572 · 2025 · External reference
Predicting high-magnitude, low-frequency crop losses using machine learning: an application to cereal crops in Ethiopia
10.1007/s10584-019-02432-7 · 2019 · External reference
Intra-growing season dry-wet spell pattern is a pivotal driver of maize yield variability in sub-Saharan Africa
10.1038/s43016-024-01040-8 · 2024 · External reference
Yield forecasting with machine learning and small data: what gains for grains?
10.1016/j.agrformet.2021.108555 · 2021 · External reference
Assessing the adoption and impact of climate-smart agricultural practices on smallholder maize farmers’ livelihoods in sub-Saharan Africa: a systematic review
10.3389/fsufs.2025.1543805 · 2025 · External reference
Maize yield effects of conservation agriculture based maize–legume cropping systems in contrasting agro-ecologies of Malawi and Mozambique
10.1007/s10705-015-9733-2 · 2015 · External reference
The impact of climate change on agricultural productivity in Asian countries: a heterogeneous panel data approach
10.1007/s11356-021-16291-2 · 2021 · External reference
A simple panel unit root test in the presence of cross-section dependence
10.1002/jae.951 · 2007 · External reference
Empirical analysis of rice and maize production under climate change in China
10.1007/s11356-020-10430-x · 2022 · External reference
Climate variation explains a third of global crop yield variability
10.1038/ncomms6989 · 2015 · External reference
Decoupling the climatic and carbon dioxide emission influence to maize crop production in Pakistan
10.1007/s11869-020-00825-7 · 2020 · External reference
Application of machine learning and neural networks to predict the yield of cereals, legumes, oilseeds and forage crops in Kazakhstan
10.3390/agriculture13061195 · 2023 · External reference
Machine learning: algorithms, real-world applications and research directions
10.1007/s42979-021-00592-x · 2021 · External reference
10.1007/978-1-4899-8008-3_9
10.1007/978-1-4899-8008-3_9 · 2014 · External reference
Explainable machine learning approach quantified the long-term (1981–2015) impact of climate and soil properties on yields of major agricultural crops across CONUS
10.3389/fsufs.2022.876404 · 2022 · External reference
Impact of climate extreme events and their causality on maize yield in South Africa
10.1038/s41598-023-39454-1 · 2023 · External reference
Risk assessment of extreme drought and extreme wetness during growth stages of major crops in China
10.3390/su17052221 · 2025 · External reference
Assessment of NDVI dynamics of maize (Zea mays L.) and its relation to grain yield in a polyfactorial experiment based on remote sensing
10.3390/agriculture13030689 · 2023 · External reference
Using NDVI for the assessment of canopy cover in agricultural crops within modelling research
10.1016/j.compag.2021.106038 · 2021 · External reference
Climate change impacts on crop yields across temperature rise thresholds and climate zones
10.1038/s41598-025-07405-8 · 2025 · External reference
Linear versus non-linear modelling: a systemic analysis with empirical data on credit and investment
10.1002/sres.3085 · 2024 · External reference
Machine learning in cereal crops disease detection: a review
10.3390/a15030075 · 2022 · External reference
Climate change and crop production nexus in Somalia: an empirical evidence from ARDL technique
10.1007/s11356-020-11846-5 · 2021 · External reference
Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions
10.1016/j.fcr.2017.11.005 · 2018 · External reference
Effect of high-temperature events when heading into the maturity period on summer maize (Zea mays L.) yield in the Huang-Huai-Hai region, China
10.3390/atmos11121291 · 2020 · External reference
A panel CUSUM test of the null of cointegration
10.1111/j.1468-0084.2004.00118.x · 2005 · External reference
Unresolved reference
2024 · External reference
Divergent responses of maize yield to precipitation in the United States
10.1088/1748-9326/ac3cee · 2021 · External reference
