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ArticleJournal articleContribution of forest resources to the livelihood of adjacent communities around forest concessions: The case of Eyumojock area, southwest region of Cameroon
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.The expectation of a healthy and buoyant future of the forests remains a mirage until community dependence on the forest is effectively compensated. Forest communities depend greatly on the forest for food, income, shelter and medicine, and will always agitate once their livelihood is greatly threatened. Objectively quantifying and sufficient compensation of community dependence on the forest is a key challenge faced while trying to balance forest use with conservation. This paper quantifies livelihood strategies for rural households, assesses the proportion of household income obtained from the forest, and explore the relationship between the quantities of NTFPs exploited and the household non-forest income in the Southwest Region of Cameroon. 471 households in Eyumojock area were sampled using semi-structured questionnaires and PRA tools. Descriptive and inferential (OLS) statistics were used for data analysis. The results reveal Agriculture (Cocoa, Plantain, Cassava), Forest (Irvingia, Bushmeat, Gnetum), Animal rearing (Pigs, Goats and Poultry) and Petty-trade as diverse income sources in order of priority (75%,17%, 1% and 7%). With a Gross annual household income in the range 420 – 51,410 $ and mean gross annual household income of 5,792 $, 81.7% of households have a gross annual income in the lower ranges of 3,636 - 7,272 $. Increase in the quantities of (Irvingia sp) Bush mango (QIV),and (Gnetum sp) Eru (QER) collected will increase household income from non - forest sources (GINF). On the other hand, increase in gross non-forest income (GINF) will reduce the quantities of Garcinia kola, Irvingia sp, Gnetum africanum, Ricinodendron heudelotii, Afrostyrax lepidophyllus, Piper nigrum L and Bushmeat significantly contribute to gross household income (p<0.05), with the quantity of Bushmeat having the greatest influence. Focusing on education and skill-building programs that will offer locals better employment opportunities, improve crop production and enhance the domestication of major NTFPs is a better option forsustainable forest management efforts. Keywords: Agriculture, bushmeat, forest concession, household income, non-timber forest product. ID: 3490013 -
ArticleJournal articleFarmer-managed natural regeneration (FMNR) on Nigeria's drylands: the influence of changing climate on agroforestry trees management
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.In Northern Nigeria, prolonged drought critically affects parkland tree regeneration and reduces land productivity and income. However, documentation of parkland trees management in extreme climatic conditions is scarce in the region. Hence, the need to evaluate the management via tree preferences by farmers on Nigeria’s dry agroecological zones (AEZ). To address this, field surveys were conducted in nine villages along a north-south transect, with precipitation increasing southwards through three AEZ of Nigeria: Sudan Savannah (SS), Northern Guinea Savannah (NGS) and Southern Guinea Savannah (SGS). Using Open Data Kit (ODK), information from 92 respondents and 4 focused groups relating to tree regeneration management were collected and aggregated. These include the abundance of the most preferred trees on farms, their regeneration potentials, management and propagation status, as well as usage and land-use constraints. Results showed drought- tolerant species (Parkia biglobosa and Mangifera indica) topped the dominant trees list across the three AEZ, both having 50% more than other agroforestry trees. Though farmers’ management strategies were highly relevant to regeneration and productivity, they did not differ across the zones (P<0.05) except at cropping outside tree canopy cover between SGS and SS using the Kruskal-Wallis test. Positive associations (0.5-0.8) between functions and preference for trees on farms for dominant agroforestry species. Farmers’ adaptation strategy to minimise risk to climate drivers and increase dry forests landscapes resilience made the driest AEZ (SS) had most species on farms and positively impacted the locals' livelihoods. Keywords: Trees, Drought, Farmers, Regeneration, Parklands ID: 3486501 -
ArticleJournal articleDeveloping simultaneously modeling systems for improving the reliability of tree aboveground biomass- carbon and its components estimates for Machilus odoratissimus nees in the central highlands, Viet Nam
XV World Forestry Congress, 2-6 May 2022
2022Also available in:
No results found.Machilus odoratissimus Nees is a multi-purpose species with, high economic value and environmental protection, so this tree species is commonly used in agroforestry models. In plantation management, it demands modeling systems that predict accurately aboveground biomass- carbon and its components. At the same time, the developed models support computing carbon accumulation of forest trees in agroforestry models for the program of reducing emissions from deforestation and forest degradation (REDD). Twenty-two 300 m2 plots were measured within the full range of 1 to 7 ages in the Central Highlands of VietNam. A total of 22 quadratic mean diameter trees were destructively sampled to obtain a dataset of the dry iomass/carbon of the stem (Bst/Cst), bark (Bba/Cba), branches (Bbr/Cbr), leaves (Ble/Cle), and total tree aboveground biomass/carbon (AGB/AGC). We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression (SUR) fit by generalized least squares for predicting tree aboveground biomass- carbon and its components. The simultaneous estimation of AGB/AGC and its components produced a higher reliability than that of the models of tree components and the total developed separately. The selected forms of modeling systems were AGB = Bst + Bba + Bbr + Ble = a1×(D2H)b1 + a2×(D2H)b2 + a3×Db3 + a4×(D2H)b4 and AGC = Cst + Cba+ Cbr + Cle = a1×(D2H)b1 ++2×(D2H)b2 + a3×Db3 + a4×(D2H)b4 (where D is the diameter at breast height and H is the height of the tree). Keywords: Agroforestry, Machilus odoratissimus, seemingly unrelated regression (SUR), tree biomass- carbon ID: 3472953
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BookletCorporate general interestEmissions due to agriculture
Global, regional and country trends 2000–2018
2021Also available in:
No results found.The FAOSTAT emissions database is composed of several data domains covering the categories of the IPCC Agriculture, Forestry and Other Land Use (AFOLU) sector of the national GHG inventory. Energy use in agriculture is additionally included as relevant to emissions from agriculture as an economic production sector under the ISIC A statistical classification, though recognizing that, in terms of IPCC, they are instead part of the Energy sector of the national GHG inventory. FAO emissions estimates are available over the period 1961–2018 for agriculture production processes from crop and livestock activities. Land use emissions and removals are generally available only for the period 1990–2019. This analytical brief focuses on overall trends over the period 2000–2018. -
BookletHigh-profileFAO Strategy on Climate Change 2022–2031 2022The FAO Strategy on Climate Change 2022–2031 was endorsed by FAO Council in June 2022. This new strategy replaces the previous strategy from 2017 to better FAO's climate action with the Strategic Framework 2022-2031, and other FAO strategies that have been developed since then. The Strategy was elaborated following an inclusive process of consultation with FAO Members, FAO staff from headquarters and decentralized offices, as well as external partners. It articulates FAO's vision for agrifood systems by 2050, around three main pillars of action: at global and regional level, at country level, and at local level. The Strategy also encourages key guiding principles for action, such as science and innovation, inclusiveness, partnerships, and access to finance.
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Book (stand-alone)Technical bookRussian Federation: Meat sector review
Country highlights prepared under the FAO/EBRD Cooperation
2014Also available in:
World food demand has seen massive changes, including a shift from staple foods to animal proteins and vegetable oils. In the short to medium term, this trend in global food demand will continue. There will be an increased demand for vegetable oils, meat, sugar, dairy products and livestock feed made from coarse grains and oilseed meals. There are numerous mid-term forecasts for the Russian Federation’s meat sector. Most of them agree on the following trends: (i) the consumption of poultry and p ork meat will increase; (ii) the consumption of beef will decrease or stabilize; and (iii) the Russian Federation will remain a net importer of meat on the world market. According to OECD and FAO projections, meat imports from the Russian Federation will decrease from 3 to 1.3 million tonnes, owing to an anticipated growth in domestic chicken meat and pork production. The country’s share in global meat imports is anticipated to decrease from 12 percent in 2006–2010, to 4 percent in 2021. While t he Russian Federation will continue to play an important role in the international meat market, it will fall from its position as the largest meat importing country in 2006–2010 to the fourth largest global meat importer by 2021, behind Japan, sub-Saharan African countries, and Saudi Arabia.