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9 results for “farm size”
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
<p>Summary:</p> <p>There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps telling where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country-level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availabilities, and climate change affect small and large farms respectively.</p> <p>The code, source data, and the simultaneously farm-size- and crop-specific harvested area datasets, including the GAEZv4 crop map based dataset and SPAM2010 crop map based dataset, are open-access, free, and available, which can be found below. The resulting dataset is available in *.csv and *.nc (netCDF) for each crop and farming system. For each crop, farming system, and farm size, we provide the gridded harvested area in the coordinate Systems of EPSG:4326 - WGS 84. Gridded summaries over crops and farming systems are also available.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>How to cite this dataset:</p> <p>Su, H., Willaarts, B., Luna-Gonzalez, D., Krol, M.S. and Hogeboom, R.J., 2022. Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries. <em>Earth System Science Data</em>, <em>14</em>(9), pp.4397-4418.</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Update history:</p> <p>I am happy to receive any questions, comments, or potential collaboration on further dataset development. Please drop your email to Han Su (h.su@utwente.nl, han_su20@163.com)</p> <p>Version 1.03.1: Fix bugs in data format; Netcdf didn't show properly before in QGIS. Data underlying the three versions are the same.</p> <p>Version 1.02: New data summary, add Netcdf data format</p> <p>Version 1: Initial dataset for peer-review, CSV format only</p> <p>-----------------------------------------------------------------------------------------------------------------------</p> <p>Note: please cite the original publications/sources if any data source based on which this dataset was developed is reused for your own study.</p> <p>SPAM2010: </p> <p>Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps, Earth System Science Data, 12, 3545-3572, 10.5194/essd-12-3545-2020, 2020.</p> <p>GAEZv4:</p> <p>FAO and IIASA: Global Agro Ecological Zones version 4 (GAEZ v4), FAO UN, Rome, Italy, 2021</p> <p>The dataset of Ricciardi et al.'s:</p> <p>Ricciardi, V., Ramankutty, N., Mehrabi, Z., Jarvis, L., and Chookolingo, B.: How much of the world's food do smallholders produce?, Global Food Security, 17, 64-72, 2018.</p> <p>The global dominant field size dataset:</p> <p>Lesiv, M., Laso Bayas, J. C., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Kumar Sahariah, P., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., and Fritz, S.: Estimating the global distribution of field size using crowdsourcing, Glob Chang Biol, 25, 174-186, 10.1111/gcb.14492, 2019.</p> <p>GLC-Share:</p> <p>Latham, J., Cumani, R., Rosati, I., and Bloise, M.: Global land cover share (GLC-SHARE) database beta-release version 1.0-2014, FAO, Rome, Italy, 2014.</p> <p>CAAS-IFPRI cropland extent map:</p> <p>Lu, M., Wu, W., You, L., See, L., Fritz, S., Yu, Q., Wei, Y., Chen, D., Yang, P., and Xue, B.: A cultivated planet in 2010 – Part 1: The global synergy cropland map, Earth System Science Data, 12, 1913-1928, 10.5194/essd-12-1913-2020, 2020.</p>
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3). in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 6. Сроки нереста приморского гребешка (1), роста и раЗвития его личинок в планктоне от начала нереста до раЗмеров 150 мкм (2) и от 150 мкм до 250–275 мкм (3). Fig. 6. Terms of spawning of the Japanese scallop (1), growth and development of its larvae in plankton from the beginning of spawning to the sizes of 150 microns (2) and from 150 microns to 250–275 microns (3).
Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 8. СвяЗь меЖду количеством личинок гребешка в планктоне раЗмером 250–275 мкм и количеством спата на коллекторах. Fig. 8. The relationship between the number of the Japanese scallop larvae in plankton with a size of 250 to 275 µm and the number of spat on collectors.
Economics Analysis of Small and Large Farm Size Honey Bee Sub-Sector in Chitwan District, Nepal
<p>This is an SPSS file that can be used for calculating various descriptive statistics and performing statistical tests such as t-tests and chi-square tests. Ranking of scale can also be carried out from these datasets.</p>
Data from: Territory size but not territorial defence varies with habitat quality and competitor density in a farming species
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Data from: Supplementing small farms with native mason bees increases strawberry size and growth rate
Pollination services, especially those of bees, play a vital role in agriculture. Declining honeybee populations require us to find alternative solutions for sustainable agriculture. Native bees are proving to be efficient pollinators. Mason bees (Osmia lignaria) provide valuable pollinator services for some woody orchard species, but their value as pollinators for herbaceous crops is largely untested. We assessed the effectiveness of O. lignaria supplementation on nine strawberry farms over two growing seasons. We specifically selected mason bees for this work because they emerge from cocoons in the springtime, when few other bees are available for pollination. Cocoons are easily deployed on farms and emerged bees have a small flight radius, so they remain localized. We placed cocoons on one side of each berry farm plot (our mason bee addition treatment) but not on the opposite side (our control). We tagged and monitored berries on nine farms throughout the growing season. We performed statistical comparisons of berries from the treatment and control for differences in berry growth rate and size. In addition, we supplemented farms with native bee homes constructed from three materials (bamboo, Phragmites and wood). This allowed us to determine whether adult mason bees would produce a subsequent generation of bees on farms and whether the bees had a preference for nest material type. Our work demonstrates that mason bees can be used successfully to pollinate herbaceous berry crops. We found that berry growth rate was significantly higher and berry volume was significantly larger for berries from the treatment relative to the control. We also found that adult bees successfully utilized the bee homes for laying the next generation of offspring and that bees colonized bamboo homes more than other home types. Synthesis and applications. Our results are the first to show that native mason bees (Osmia lignaria) can be used successfully to provide pollination services on strawberry farms. Their use results in the production of bigger berries and faster berry growth rates than managed honeybees alone. Mason bees overwinter (and can be purchased) in cocoons, offering great potential for efficient and effective pollination services, for a variety of agricultural applications across different geographical regions. The availability of suitable nesting sites and protection of subsequent generations of cocoons from wasp parasitization warrant future consideration.
Primary Survey Socioeconomic Dataset of the Study on Agricultural Performance and Farm Size: Village Dynamics in South Asia (VDSA)
<p>These datasets contain many economic variables related to agriculture like crop output value, profit and several others. These datasets can be used for testing several hypotheses related to agricultural economics, both at plot level and household level.</p> <p>Users can also reproduce these datasets using the STATA 14 do file ‘VDSA data management for agricultural performance’. This STATA program file uses the Village Dynamics in South Asia (VDSA) raw data files in excel format. The resulting output will be two data files in stata format, one at plot level and other at household level. </p> <p>These plot level and household level data sets are also included in this repository. The word file ‘guidelines’ contain instructions to extract VDSA raw data from VDSA knowledge bank and use them as inputs to run the STATA do file ‘VDSA data management for agricultural performance’ </p> <p>The VDSA raw data files in excel format needed to run the stata do file are also available in this repository for users convenience</p> <p>The raw VDSA data were generated by the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in partnership with Indian Council of Agricultural Research (ICAR) Institutes and the International Rice Research Institute (IRRI) and funded by the Bill & Melinda Gates Foundation (BMGF) (Grant ID: 51937). The data were acquired in surveys by resident field investigators. Data collection was mostly through paper based questionnaires and Samsung tablets were also used since 2012. The survey instruments used for different modules are available at <a href="http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx">http://vdsa.icrisat.ac.in/vdsa-questionaires.aspx</a></p> <p>Study sites were selected using a stepwise purposive sampling covering agro-ecological diversity of the region. Three districts within each zone were selected based on soil, climate parameters as well as the share of agricultural land under ICRISAT mandate crops. On similar lines, one typical sub-district within each district and two villages within each sub-district were selected. Within each village, ten random households from four landholding groups were selected.</p> <p>Selected farmers were visited by well trained, agriculture graduate, resident field investigators, once every three weeks to collect information related to various socioeconomic indicators. Some of the data modules like details on crop cultivation activities including plot wise input, output was collected every three weeks while others like general endowments were collected once at the beginning of every agricultural year. </p> <p>The compiled data, source data, data descriptions and data management code are all published in a public repository at <a href="http://dataverse.icrisat.org/dataverse/socialscience">http://dataverse.icrisat.org/dataverse/socialscience</a> at <a href="https://doi.org/10.21421/D2/HDEUKU">https://doi.org/10.21421/D2/HDEUKU</a>]</p> <p>Some of the several benefits of these data are:</p> <ul> <li>Scientists, students, development practitioners can benefit from these data to track changes in the livelihood options of the rural poor as this data provides long-term, multi-generational perspective on agricultural, social and economic change in rural livelihoods.</li> <li>The survey sites provide a socio-economic field laboratory for teaching and training students and researchers</li> <li>This dataset can be used for diverse agricultural, development and socio-economic analysis and to better understand the dynamics of Indian agriculture.</li> <li>The data helps to provide feedback for designing policy interventions, setting research priorities and refining technologies.</li> <li>Shed light on the pathways in which new technologies, policies, and programs impact poverty, village economies, and societies</li> </ul>
Data from: Supplementing small farms with native mason bees increases strawberry size and growth rate
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Data from: Free-ranging farm cats: home range size and predation on a livestock unit in Northwest Georgia
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