Drivers of on farm tree diversity contribute to climate change

mitigation, adaptation and resilience

By: Musana S. Bernard, Ndayamabaje J.D., Ndoli A., Mukuralinda A., Safari D., Mbonigaba J.J.

Outline• Problem statement• Land cover changes facts• Forest degradation risk factors• How on farm tree diversity impacts climate resilience • Methodology• Species present in the study area• Results of the Regression analysis• Discussion• Relations between feeding systems, feeds and feeds costs• Conclusion and recommendation

Problem statement• Considering the last 100 years landscapes history, an impressive

biodiversity erosion in tree stratum has dramatically change the availability of high value wood, wild fruits, medicinal plants and cultural plants.• Fortunately since 1986 a consciousness of endangered species has develop

a philosophy of Agroforestry that can be considered at some extend as a transition toward recovering some of the important products and services (Konig, 1992; Kalinganire, 1996; Oksanen, 1997; Dixon,2003)• AF have moved slowly due to poor understand socio-economic drivers and

institutionals barriers to adoption (Mukuralinda et al.,2015; Bucagu et al.,2013)

4

Land cover changes factsCareful interpretation of biomass based indicators (NDVI is important)• Loss in NDVI (biomass) in forest area is degradation/deforestation• Gain in NDVI (biomass) in savanna ecosystems could be again degradation due to bush

encroachment

Forest degradation risk factors

Meta-analysis of Geist & Lambin 2004: “Our results show that desertification is driven by a limited suite of recurrent core variables”

How on farm tree diversity impacts climate resilience • Tree diversity as indicator of tree adoption• Tree diversity and agro-ecology• Diversity of trees and sustainability of wood production• Diversity of trees and soil biota diversity (Barrios,2014)• Diversity of trees and overall agri-system diversity (Barrios,2014)• Biodiversity and agriculture

Methodology• 145 farmers have been interview (10-30 farmers/sectors) in 2 Districts • The relationship of key household characteristic with the management of trees and

Diversity of trees in the• farm has been investigated from household level information. 42 regressions have

been conducted using linear model (Gaussian model) and quasi-binomial regression using the generalized linear model following the algorithm of the glm package in R. The details of the regression and the pseudo – R2 and the R syntax used are presented in appendix of the report. To simplify the Interpretation results are presented in 3 group regressions:• Regression Analysis of Tree Diversity• Regression Analysis of Tree management • Regression analysis for Tree management and Diversity

Selected sites: Gatsibo District

Selected sites: Bugesera district

Results

Species present in the study area

Mangifera Indica

Grevillea robusta

Persea americana

Citrus SP

Carica Papaya

Eucalyptus SP

Markhamia Lutea

Citrus Lemon

Citrus Sinensis

Calliandra

11%

9%

7%

5%

5%

5%

4%

4%

4%

2%• 90% of the top tree

species in the east are exotic species• Markhamia lutea is the

first indigenous species present in farmer’s land

Results of the Regression analysis• The 3 regression were constructed from logistic regression that has a

dependent or independent variables numerical (continuous) or categorical variables (discrete variables). • The first type having quantitative dependent variable, it has been

presented in tabular format and discussed. The covariate type of response and qualitative response are too complex to assist decision making because it needs definitions of Dummy variables and complex transformation.

R2(1) Intercept

Variables

Coeff T value P value Coeff T value P value

SUBCOUNTY 0.086.01639 13.782 <2e-16

***Nyamata -2.91639 -2.507 0.0133 *

Rweru -3.01639 -2.478 0.0144 *

security5.1296 15.448 <2e-16

***Yes 1.6620 2.134 0.0347 *

month_secured -Qtt 0.005.15901 7.477 6.98e-12

***0.04161 0.451 0.655ns

tenure 0.04 ns ns

Training 0.173.5098 7.86 8.45e-13

***Yes 2.9796 5.372 3.08e-07 ***

Distancet-Qtt na6.22082 13.319 <2e-16

***0.07178 1.221 0.228(ns)

Livestock 0.043.88 5.67 7.62e-08

***Yes 1.8867 2.508 0.0133 *

Income source 0.47 5.00 10.701 <2.00E-16***

(income,1,11)* 2 3.495 0.000484***

(income,1,2)* 4.667 7.737 1.57E-14*** (income,1,2,5)* 1 1.888 0.059225. (income,1,3,6)* 4.667 7.737 1.57E-14*** (income,1,6)* -1.333 -2.21 0.02718* (income,6,11)* -3 -5.243 1.74E-07***

Results of the Regression analysis (2)

R2(1) Intercept

Variables

Income source + Average income(RWF)-

Qtt

0.50 9.97

22.849 < 2e-16 (income,1,11)*-3.062 -5.722 1.22E-08***

(income,1,2,3)*-5.366 -10.82 < 2e-16*** (income,1,2,3,11)*-5.592 -11.065 < 2e-16*** (income,1,2,5)*-4.058 -8.192 4.66E-08*** (income,1,2,7)*-5.023 -9.727 < 2e-16*** (income,1,2,7,11)*-5.385 -9.552 < 2e-16*** (income,1,3)*-5.549 -10.375 < 2e-16*** (income,1,6)*-6.61 -11.488 < 2e-16*** (income,1,7)*-5.487 -11.703 < 2e-16*** (income,2,7)*-6.093 -10.3 < 2e-16*** (income,6,11)*-8.13 -15.132 < 2e-16*** (income,7)*-6.278 -13.518 < 2e-16*** (incomerwf)*0.000001118 2.361 0.018303*

Results of the Regression analysis (3)

- Most of farmers interviewed have income that is below 300,000 RWF;

- The number of income does not explain the diversity but diversity above 10 tree species are seen where farmers have more than one source of income

• Income is not clearly related diversity

Discussion

Discussion(2)• e.g. Nyamata Revenue and Diversity not correlated:

Despite high revenue Nyamata has a reduced diversity in general

• There is trend of number of income has a diversity

• Location and diversity indicates that in Nyamata and Rweru sectors, there is a reduction of almost 50% of tree diversity. • It has also been observed that for area with 5month of food security

the diversity reaches 6 species and for 10 month it reaches 7 species. • Land tenure was found not significantly correlated with tree diversity• Training on Natural regeneration was significantly correlated to

diversity (3 species than the non-trained)• Development of livestock may induce increase of diversity

• More than 50% of the variability of on farm tree diversity could be explained by the details of income sources and average annual income.• Among farmers who rely on tree products : Farmers who have

addition income such as wages, salaries or casual labor tend to reduce the tree diversity; while those who depend more on their products from their farms (food products) owned the highest tree diversity. • The group with the highest diversity has at least 6 species on average.

Farmers with livestock have 48% more species diversity compared to those who does not have livestock.

Relations between feeding systems, feeds and feeds costs

Conclusion and Recommendations:• More detailed typology of

farmer is needed to achieved both increase tree coverage and tree diversity (location-accessibility, income source, income, livestock activities and feed availability)• More Training is need to natural

regenerate trees on farm

• Diversifying on-farm income through integrated crop-livestock systems would increase the adoption of more tree species. • Specialization of tree value chain

is crucial to increase diversity on farm particularly for farmer are more interested on tree products and services

Thank you