1.
Le, Q. B., Nkonya, E. & Mirzabaev, A. Biomass productivity-based mapping of global land degradation hotspots. Econ. Land Degrad. Improve. Glob. Assess. Sustain. Dev. https://doi.org/10.1007/978-3-319-19168-3_4 (2016).
Article
Google Scholar
2.
IPBES. The IPBES assessment report on land degradation and restoration. Secrateriat Intergov. Sci. Platf. Biodivers. Ecosyst. Serv. https://doi.org/10.5281/ZENODO.3237393 (2018).
3.
Webb, N. P. et al. Land degradation and climate change: Building climate resilience in agriculture. Front. Ecol. Environ. 15, 450–259 (2017).
Article
Google Scholar
4.
Mbow, C., Smith, P., Skole, D., Duguma, L. & Bustamante, M. Achieving mitigation and adaptation to climate change through sustainable agroforestry practices in Africa. Curr. Opin. Environ. Sustain. 6, 8–14 (2014).
Article
Google Scholar
5.
Waswa, B. S. et al. Geoderma Evaluating indicators of land degradation in smallholder farming systems of western Kenya. Geoderma 195–196, 192–200 (2013).
ADS
Article
Google Scholar
6.
Lasco, R. D., Delfino, R. J. P., Catacutan, D. C., Simelton, E. S. & Wilson, D. M. Climate risk adaptation by smallholder farmers : The roles of trees and agroforestry. Curr. Opin. Environ. Sustain. 6, 83–88 (2014).
Article
Google Scholar
7.
Thorlakson, T., Neufeldt, H. Reducing subsistence farmers’ vulnerability to climate change: evaluating the potential contributions of agroforestry in western Kenya. Agric & Food Secur. 1, 15 (2012).
Article
Google Scholar
8.
De Giusti, G., Kristjanson, P. & Rufino, M.C. Agroforestry as a climate change mitigation practice in smallholder farming: evidence from Kenya. Climatic Change 153, 379–394 (2019).
ADS
Article
Google Scholar
9.
Meijer, S. S., Catacutan, D., Ajayi, O. C. & Sileshi, G. W. The role of knowledge, attitudes and perceptions in the uptake of agricultural and agroforestry innovations among smallholder farmers in sub- Saharan Africa. Int. J. Agric. Sustain. 13, 40–54 (2015).
Article
Google Scholar
10.
Henry, M. et al. Agriculture, ecosystems and environment biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya. Agric. Ecosyt. Environ. 129, 238–252 (2009).
CAS
Article
Google Scholar
11.
Jindal, R., Swallow, B. & Kerr, J. Forestry-based carbon sequestration projects in Africa : Potential benefits and challenges. Nat. Resour. Forum 32, 116–130 (2008).
Article
Google Scholar
12.
Estrada, M. & Corbera, E. The potential of carbon offsetting projects in the forestry sector for poverty reduction in developing countries. in Integrating Ecology and Poverty Reduction: The Application of Ecology in Development Solutions (eds. Ingram, J. C., DeClerck, F. & del Rio, C.) 137–147. https://doi.org/10.1007/978-1-4614-0186-5_11 (Springer, 2011).
13.
Willemen, L. et al. How to halt the global decline of lands. Nat. Sustain. 3, 164–166 (2020).
Article
Google Scholar
14.
Reed, M. S. Participatory technology development for agroforestry extension: An innovation-decision approach. Afr. J. Agric. Res. 2, 334–341 (2007).
Google Scholar
15.
Shames, S., Wollenberg, E., Buck, L. E., Kristjanson, P. & Masiga, M. Institutional Innovations in African Smallholder Carbon Projects. CCAFS Report No. 8 Copenhagen, Denmark: CCAFS (2012).
16.
TIST. TIST Program Summary: Kenya. http://www.tist.org/tist/docs/USAID-Documents/TIST{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Program{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Summary{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20KE{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20200101.pdf (2021).
17.
I4EI. USAID Kenya TIST Program Final Performance Report. http://www.tist.org/tist/docs/USAID-Documents/I4EI{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20USAID{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20KE{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20140616{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Final{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Report{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20140619.pdf (2014).
18.
Oppenheimer, S. Impact Evaluation of the TIST Program in Kenya. 1–38. http://www.tist.org/tist/docs/PDD-Documents/TIST{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20KE{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20PD-VCS-Ex23{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20GL2{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Community{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Survey{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Result.pdf (2011).
19.
CAAC. Monitoring Report For TIST Program in Kenya. VCS-009, Verification 03. http://www.tist.org/tist/docs/PDD-Documents/TIST{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20KE{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20PD-VCS-009n{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20App13{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Verif{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}2003{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Monitoring{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20Rpt{6d6906d986cb38e604952ede6d65f3d49470e23f1a526661621333fa74363c48}20200522-2.pdf (2020).
20.
Jose, S. Agroforestry for ecosystem services and environmental benefits: an overview. Agrofor. Syst. 76, 1–10 (2009).
Article
Google Scholar
21.
Mafongoya, P. et al. Maize productivity and pro fi tability in Conservation Agriculture systems across agro-ecological regions in Zimbabwe : A review of knowledge and practice. Agric. Ecosyst. Environ. 220, 211–225 (2016).
Article
Google Scholar
22.
Wang, J. et al. Relations between NDVI and tree productivity in the central Great Plains. Int. J. Remote Sens. 25, 3127–3138 (2004).
ADS
Article
Google Scholar
23.
Gichenje, H. & Godinho, S. Establishing a land degradation neutrality national baseline through trend analysis of GIMMS NDVI time-series. Land Degrad. Dev. 29, 2985–2997 (2018).
Article
Google Scholar
24.
Fensholt, R. & Proud, S. R. Evaluation of earth observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 119, 131–147 (2012).
ADS
Article
Google Scholar
25.
Borish, D., King, N. & Dewey, C. Enhanced community capital from primary school feeding and agroforestry program in Kenya. Int. J. Educ. Dev. 52, 10–18 (2017).
Article
Google Scholar
26.
De Jong, B. H. J., Bazán, E. E. & Montalvo, S. Application of the “Climafor” baseline to determine leakage : The case of Scolel Te. Mitig. Adapt. Strateg. Glob. Chang. 12, 1153–1168 (2007).
Article
Google Scholar
27.
Ilstedt, U. et al. Intermediate tree cover can maximize groundwater recharge in the seasonally dry tropics. Sci. Rep. 6, 1–12 (2016).
Article
Google Scholar
28.
Ndayambaje, J. D. & Mohren, G. M. J. Fuelwood demand and supply in Rwanda and the role of agroforestry. Agrofor. Syst. 83, 303–320 (2011).
Article
Google Scholar
29.
Iiyama, M. et al. The potential of agroforestry in the provision of sustainable woodfuel in sub-Saharan Africa. Curr. Opin. Environ. Sustain. 6, 138–147 (2014).
Article
Google Scholar
30.
TIST. The Tree: TIST Uganda December 2011 Newsletter. (2011). https://program.tist.org/uganda-newsletters. Accessed 5 Feb 2021.
31.
TIST. Mazingira Bora: TIST Kenya January 2012 Newsletter. (2012). https://program.tist.org/kenya-newsletters. Accessed 5 Feb 2021.
32.
Zhang, Y. et al. Multiple afforestation programs accelerate the greenness in the ‘Three North’ region of China from 1982 to 2013. Ecol. Indic. 61, 404–412 (2016).
Article
Google Scholar
33.
Holl, B. N. & Brancalion, P. H. Tree planting is not a simple solution. Science (80) 368, 580–582 (2020).
ADS
CAS
Article
Google Scholar
34.
Lenton, T. M. Tipping positive change. Philos. Trans. R. Soc. B Biol. Sci. 375, 1–2 (2020).
Article
Google Scholar
35.
Eckert, S., Kiteme, B., Njuguna, E. & Zaehringer, J. G. Agricultural expansion and intensification in the foothills of Mount Kenya : A landscape perspective. Remote Sens. 9, 784 (2017).
ADS
Article
Google Scholar
36.
Schmocker, J., Liniger, H. P., Ngeru, J. N., Brugnara, Y. & Auchmann, R. Trends in mean and extreme precipitation in the Mount Kenya region from observations and reanalyses. Int. J. Climatol. 1514, 1500–1514 (2016).
Article
Google Scholar
37.
FAO. Africover Multipurpose Land Cover Database for Kenya. (2000). https://datasets.wri.org/dataset/agricultural-areas-in-kenya. Accessed 4 June 2020.
38.
Williams, D. L., Goward, S. & Arvidson, T. Landsat: Yesterday, today, and tomorrow. Photogramm. Eng. Remote Sens. 72, 1171–1178 (2006).
Article
Google Scholar
39.
Holben, B. N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434 (1986).
ADS
Article
Google Scholar
40.
Gorelick, N. et al. Remote sensing of environment google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
ADS
Article
Google Scholar
41.
QGIS.org. QGIS Geographic Information System. QGIS Association. Version 3.8.2. http://www.qgis.org (2021).
42.
R Core Team. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Version 3.6.3. https://www.R-project.org (2021).