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    The vegetation survey was performed in 21 circular plots of 10-m radius (250 m2) distributed as follows: 2 plots located in the area undergoing revegetation since 2014, 2 plots on the area undergoing revegetation since 2010, 6 plots on the area undergoing revegetation since 2006, and 11 plots on the area undergoing revegetation since 2002. Trees with diameter at breast height in the range of 2.5 – 10 cm were measured within the plots. In total, 717 trees were selected for measurement of diameter at breast height, wood density, total height and species identification. The coordinates of the center of each plot were recorded using a portable GPS (Garmin eTrex 10, Germany). The angle and distance of each tree from the center of the plot were measured, and its coordinates estimated. Stem diameters of all trees within the plot were measured using a calliper. Tree height was measured using a Vertex IV (Haglöf, Sweden). Crown diameter was measured in north-south and east-west directions. For species identification, a preliminary analysis of the flora of the area was performed following the guidelines developed by Idárraga-Piedrahita et al. (2011), Cardona et al. (2010) and Cardona et al. (2011). For the identification of the trees, leaf samples were collected from all individuals. The samples were preserved in alcohol for transportation to the laboratory, and subsequently dried and visually matched with species of the herbarium of the National University of Colombia. Some species could only be classified by genus because their reproductive morphology could not be determined, which is specifically relevant for the identification of Vismia sp. (Hypericaceae), Casearia sp. (Salicaceae), Cordia sp. (Boraginaceae) and Licania sp. (Chrysobalanaceae). Due to the complexity of their morphological and reproductive characteristics, genera Inga sp. (Fabaceae), Citrus sp. (Rutaceae), Ficus sp. (Moraceae) and Piper sp. (Piperaceae) were not classified at the species level. Herbaceous aboveground biomass growing spontaneously within the area and that was located outside of areas with forest cover was harvested at ground level in 120 plots (4 m2 each) with 30 plots per area undergoing restoration since 2014, 2010, 2006 and 2002. The total fresh weight of the biomass was determined in the field, and a subsample of 500 g was oven-dried for 24 – 48 hours for determination of dry weight.

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    The data was conducted by three Organizations: EDRI, IFPRI and University of Sussex to see the Impact of biomassweb on the economies of developing countries using the 2005 Household Income Consumption Expenditure Survey. It covers 65 production accounts, 100 consumption accounts, 16 households, 4 factors of production and, government, I-S and ROW accounts.

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    The survey covers 227 small holder maize growing farmers in Bako, Jimma Arjo and Yayu in the Oromia region. The baseline information covers the farming household characteristics (fam size, sources of income, education levels etc), maize and livestock production challenges, access to extension and markets.

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    The yield gap for maize in two regions (Ashanti Region and Brong-Ahafo region) in Ghana has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field.

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    The file describes tree diameters at breast height (DBH) and heights measurements of green spaces in Kumasi Ghana. The elevation and ground distance data are used to calculate the actual heights of trees. Both DBH and height data are then used to estimate tree biomass using appropriate biomass allometric equations.

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    Agronomic Fertilizer Use Efficiency (WUE) for maize biomass in Ashanti region and Brong-Ahafo region in Ghana has been estimated under three different fertilizer application rates using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management.

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    Agronomic Radiation Use Efficiency (RUE) for maize biomass in Ashanti region and Brong-Ahafo region in Ghana has been estimated under three different fertilizer application rates using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management.

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    The yield gap for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The yield gap of a crop grown in a certain location and cropping system is defined as the difference between the yield and biomass under optimum management and the average yield achieved by farmers. Yield under optimum management is labeled as potential yield (Yp) under irrigated conditions or water-limited potential yield (Yw) under rain-fed conditions.Yp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Yp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Yw, equivalent to water-limited potential yield, is the most relevant benchmark. Both Yp and Yw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field.

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    The stover biomass for maize across the Ethiopia has been estimated using crop model LINTUL5 embedded into the modeling framework SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management. The stover biomass gap of a crop grown in a certain location and cropping system is defined as the difference between the stover biomass under optimum management and the average stover biomass achieved by farmers. Stover biomass under optimum management is labeled as potential biomass (Bp) under irrigated conditions or water-limited potential biomass (Bw) under rain-fed conditions.Bp is location specific because of the climate, and not dependent on soil properties assuming that the required water and nutrients are non-limiting and can be added through management. Thus, in areas without major soil constraints, Bp is the most relevant benchmark for irrigated systems. Whereas, for rain-fed crops, Bw, equivalent to water-limited potential biomass, is the most relevant benchmark. Both Bp and Bw are calculated for optimum planting dates, planting density and region-specific crop variety which is critical in determining the feasible growth duration, particularly in tropical climatic conditions where two or even three crops are produced each year on the same field.

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    This data (Primary Data for BiomassWeb WP4.5) is on agricultural land use and resource management practices in small-scale maize systems in Bolgatanga and Bongo Districts, Upper East Region, Ghana. The data were collected by Francis Molua Mwambo, using semi-structured questionnaire to interview small-scale maize farmers in 2015. The data are based on the interviewees' estimate which were provided as feedback to the questionnaire.