Allocating community-level payments for ecosystem services initial experiences from a REDD pilot in Tanzania

Payments for ecosystem services (PES) typically reward landowners for managing their land to provide ecosystem services that would not otherwise be provided. REDD—Reduced Emissions from Deforestation and Forest Degradation—is a form of PES aimed at decreasing carbon emissions from forest conversion and extraction in lower-income countries. A key challenge for REDD occurs when it is implemented at the community rather than the individual landowner level. Whilst achieving this community-level reduction relies on individuals changing their interaction with the forest, incentives are not aligned explicitly at the individual level. Rather, payments are made to the community as a single entity in exchange for verified reduced forest loss, as per a PES scheme. In this paper, we explore how community level REDD has been implemented in one multiple-village pilot in Tanzania. Our findings suggest that considerable attention has been paid to monitoring, reporting, verification, and equity. Though no explicit mechanism ensures individual compliance with the group PES, the development of village level institutions, “social fencing,” and a shared future through equal REDD payments factor into community decisions that influence the level of community compliance that the program will eventually achieve. However, few villages allocate funds for explicit enforcement efforts to protect the forest from illegal activities undertaken by outsiders.

Equations to predict methane emissions from cows fed at maintenance energy level in pasture-based systems

Ruminant production is a vital part of food industry but it raises environmental concerns, partly due to the associated methane outputs. Efficient methane mitigation and estimation of emissions from ruminants requires accurate prediction tools. Equations recommended by international organizations or scientific studies have been developed with animals fed conserved forages and concentrates and may be used with caution for grazing cattle. The aim of the current study was to develop prediction equations with animals fed fresh grass in order to be more suitable to pasture-based systems and for animals at lower feeding levels. A study with 25 nonpregnant nonlactating cows fed solely fresh-cut grass at maintenance energy level was performed over two consecutive grazing seasons. Grass of broad feeding quality, due to contrasting harvest dates, maturity, fertilisation and grass varieties, from eight swards was offered. Cows were offered the experimental diets for at least 2 weeks before housed in calorimetric chambers over 3 consecutive days with feed intake measurements and total urine and faeces collections performed daily. Methane emissions were measured over the last 2 days. Prediction models were developed from 100 3-day averaged records. Internal validation of these equations, and those recommended in literature, was performed. The existing in greenhouse gas inventories models under-estimated methane emissions from animals fed fresh-cut grass at maintenance while the new models, using the same predictors, improved prediction accuracy. Error in methane outputs prediction was decreased when grass nutrient, metabolisable energy and digestible organic matter concentrations were added as predictors to equations already containing dry matter or energy intakes, possibly because they explain feed digestibility and the type of energy-supplying nutrients more efficiently. Predictions based on readily available farm-level data, such as liveweight and grass nutrient concentrations were also generated and performed satisfactorily. New models may be recommended for predictions of methane emissions from grazing cattle at maintenance or low feeding levels.