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​​This book provides standards and guidelines for quantifying greenhouse gas emissions and removals in smallholder agricultural systems and comparing options for climate change mitigation based on emission reductions and livelihood trade-offs. Globally, agriculture is directly responsible for about 11% of annual greenhouse gas (GHG) emissions and induces an additional 17% through land use change, mostly in developing countries. Farms in the developing countries of sub-Saharan Africa and Asia are predominately managed by smallholders, with 80% of land holdings smaller than ten hectares. However, little to no information exists on greenhouse gas emissions and mitigation potentials in smallholder agriculture. Greenhouse gas measurements in agriculture are expensive, time consuming, and error prone, challenges only exacerbated by the heterogeneity of smallholder systems and landscapes. Concerns over methodological rigor, measurement costs, and the diversity of approaches, coupled with the demand for robust information suggest it is germane for the scientific community to establish standards of measurements for quantifying GHG emissions from smallholder agriculture. Standard guidelines for use by scientists, development organizations will help generate reliable data on emissions baselines and allow rigorous comparisons of mitigation options. The guidelines described in this book, developed by the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) and partners, are intended to inform anyone conducting field measurements of agricultural greenhouse gas sources and sinks, especially to develop IPCC Tier 2 emission factors or to compare mitigation options in smallholder systems.
The National Agricultural Statistics Service (NASS) is the primary statistical data collection agency within the U.S. Department of Agriculture (USDA). NASS conducts hundreds of surveys each year and prepares reports covering virtually every aspect of U.S. agriculture. Among the small-area estimates produced by NASS are county-level estimates for crops (planted acres, harvested acres, production, and yield by commodity) and for cash rental rates for irrigated cropland, nonirrigated cropland, and permanent pastureland. Key users of these county-level estimates include USDA's Farm Services Agency (FSA) and Risk Management Agency (RMA), which use the estimates as part of their processes for distributing farm subsidies and providing farm insurance, respectively. Improving Crop Estimates by Integrating Multiple Data Sources assesses county-level crop and cash rents estimates, and offers recommendations on methods for integrating data sources to provide more precise county-level estimates of acreage and yield for major crops and of cash rents by land use. This report considers technical issues involved in using the available data sources, such as methods for integrating the data, the assumptions underpinning the use of each source, the robustness of the resulting estimates, and the properties of desirable estimates of uncertainty.
Here in one easy-to-understand volume are the statistical procedures and techniques the agricultural researcher needs to know in order to design, implement, analyze, and interpret the results of most experiments with crops. Designed specifically for the non-statistician, this valuable guide focuses on the practical problems of the field researcher. Throughout, it emphasizes the use of statistics as a tool of research—one that will help pinpoint research problems and select remedial measures. Whenever possible, mathematical formulations and statistical jargon are avoided. Originally published by the International Rice Research Institute, this widely respected guide has been totally updated and much expanded in this Second Edition. It now features new chapters on the analysis of multi-observation data and experiments conducted over time and space. Also included is a chapter on experiments in farmers' fields, a subject of major concern in developing countries where agricultural research is commonly conducted outside experiment stations. Statistical Procedures for Agricultural Research, Second Edition will prove equally useful to students and professional researchers in all agricultural and biological disciplines. A wealth of examples of actual experiments help readers to choose the statistical method best suited for their needs, and enable even the most complicated procedures to be easily understood and directly applied. An International Rice Research Institute Book
Agricultural Statistics is published each year to meet the diverse need for a reliable reference book on agricultural production, supplies, consumption, facilities, costs, and returns. Its tables of annual data cover a wide variety of facts in forms suited to most common use. The estimates for crops, livestock, and poultry made by the U.S. Department of Agriculture are prepared mainly to give timely current state and national totals and averages. They are based on data obtained by sample surveys of farmers and of people who do business with farmers. The survey data are supplemented by information from the Census of Agriculture taken every five years. Being estimates, they are subject to revision as more data become available from commercial or government sources. Unless otherwise indicated, the totals for the United States shown in the various tables on area, production, numbers, price, value, supplies, and disposition are based on official Department estimates. They exclude states for which no official estimates are compiled. Extensive data includes statistics for the following: -Grain and Feed -Cotton, Tobacco, Sugar Crops, and Honey -Oilseeds, Fats, and Oils -Vegetables and Melons -Hay, Seeds, and Minor Field Crops -Cattle, Hogs, and Sheep -Dairy and Poultry -Insurance, Credit & Cooperatives -Agricultural Conservation & Forestry -Consumption & Family Living -Fertilizers & Pesticides Miscellaneous Agricultural Statistics such as Foreign Agricultural Trade Statistics including exports, fisheries and more. Professionals in the following fields to include farmers, ranchers, soil conservationists, surveyors, agricultural economist consultants, livestock manufacturers, livestock feedlot operators, food distributors, animal scientists, food chemists, food brokers, farm and land appraisers (and more) may have the greatest interest in this volume.
The variety and abundance of qualitative characteristics of agricultural products have been the main reasons for the development of different types of non-destructive methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. Applications of Image Processing and Soft Computing Systems in Agriculture examines applications of artificial intelligence in agriculture and the main uses of shape analysis on agricultural products such as relationships between form and genetics, adaptation, product characteristics, and product sorting. Additionally, it provides insights developed through computer vision techniques. Highlighting such topics as deep learning, agribusiness, and augmented reality, it is designed for academicians, researchers, agricultural practitioners, and industry professionals.
A substantial portion of the world's rice is produced and consumed in the Asia and Pacific region. As much of the region's population depends on rice farming for consumption and livelihood, the demand for good quality statistics remains essential for effective policy formulation. Recent advances in remote sensing serve as a viable alternative to traditional methods of compiling agricultural statistics by responding to the emerging data requirements and overall needs of modern agriculture, especially for staple commodities such as rice. This publication outlines the procedures, methods and tools developed for generating statistics on paddy rice area and production using remote sensing and GIS techniques. It is intended to encourage and assist national statistical systems in piloting and adopting satellite-based techniques as an alternative to existing data collection methods, while enabling other interested readers to comprehend whether the adoption of these methodologies can improve the quality and timeliness of agricultural statistics. The handbook also serves as a complement to a massive online open course developed by ADB for crop area estimation.