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This ISFM handbook is organized into seven sections that include: an introduction, the need for ISFM, the principles of ISFM, soil fertility management practices, targeting ISFM options, an introduction to soil and crop production and a section containing tables, definitions and reference information. The entire project team that includes the TAG hopes that the reader finds this handbook a useful tool for tackling soil fertility and management on the continent and elsewhere where similar factors of production are at play.

fertilizers --- production --- Africa --- cropping systems --- fertilisers --- reference standards --- application rates --- best management practices --- crop yield --- nutrients --- crop production --- handbooks --- Africa South of Sahara --- subsaharan Africa --- soil fertility agricultural systems --- farming systems --- manuals

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This open-access textbook provides a comprehensive, up-to-date guide for students and practitioners wishing to access in a single volume the key disciplines and principles of wheat breeding. Wheat is a cornerstone of food security: it is the most widely grown of any crop and provides 20% of all human calories and protein. The authorship of this book includes world class researchers and breeders whose expertise spans cutting-edge academic science all the way to impacts in farmers’ fields. The book’s themes and authors were selected to provide a didactic work that considers the background to wheat improvement, current mainstream breeding approaches, and translational research and avant garde technologies that enable new breakthroughs in science to impact productivity. While the volume provides an overview for professionals interested in wheat, many of the ideas and methods presented are equally relevant to small grain cereals and crop improvement in general. The book is affordable, and because it is open access, can be readily shared and translated -- in whole or in part -- to university classes, members of breeding teams (from directors to technicians), conference participants, extension agents and farmers. Given the challenges currently faced by academia, industry and national wheat programs to produce higher crop yields --- often with less inputs and under increasingly harsher climates -- this volume is a timely addition to their toolkit.

Agricultural genome mapping. --- Agriculture. --- Food science. --- Medicine—Research. --- Biology—Research. --- Agricultural Genetics. --- Food Science. --- Translational Research. --- Food technology --- Chemical engineering --- Farming --- Husbandry --- Industrial arts --- Life sciences --- Food supply --- Land use, Rural --- Gene mapping --- crop breeding --- breeding technologies --- genetic gains --- crop yield potential --- wheat pathology --- global economics of wheat --- agriculture public-private parternship --- Blat --- Genètica vegetal --- Millorament selectiu de plantes

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The agricultural community is face with the challenge of increasing food production by more than 70% to meet demand from the global population increase by the mid-21st century. Sustainable food production involves the sustained availability of resources, such as water and energy, to agriculture. The key challenges to sustainable food production are population increase, increasing demands for food, climate change, climate variability, and decreasing per capita land and water resources. To discuss more details on (a) the challenges for sustainable food production and (b) mitigation options available, a Special Issue on “Water Management for Sustainable Food Production” was assembled. This Special Issue focused on issues such as irrigation using brackish water, virtual water trade, allocation of water resources, consequences of excess precipitation on crop yields, strategies to increase water productivity, rainwater harvesting, irrigation water management, deficit irrigation, fertilization, environmental and socio-economic impacts, and irrigation water quality. The articles in the Special Issue cover several water-related issues across the U.S., Asia, Middle East, Africa, and Pakistan concerning sustainable food production. The articles in this Special Issue highlight the substantial impacts on agricultural production, water availability, and water quality in the face of increasing demands for food and energy.

AquaCrop model --- capillary rise --- climate change --- rainfall variability --- supplemental irrigation --- crop growth --- lettuce --- AquaCrop --- water saving --- water productivity --- deficit irrigation --- nitrogen productivity --- fertigation --- drip irrigation --- low-discharge --- arid regions --- Africa --- food security --- system of rice intensification --- water conservation --- climate variability --- water use efficiency --- multi-crop production --- pressure irrigation systems --- water costs --- corn --- soybeans --- maize --- crop-water production function --- West Africa --- spatiotemporal rainfall variability --- tied ridges --- scattered plots --- pearl millet --- yield loss --- crop uptake --- food quality --- geogenic --- emerging contaminants --- nanomaterials --- lysimeter --- canola --- water table --- root distribution --- evapotranspiration --- sustainable irrigation --- bibliometric analysis --- innovation and technology --- unconventional water resources --- delayed transplanting --- seedling age --- seedling density --- wet season --- grain sorghum --- precipitation --- rainfed --- multiple linear regression --- crop yield --- principal component analysis --- water allocation --- WEAP model --- scenario --- Awash River Basin --- sustainability --- agriculture --- virtual water trade --- blue --- green --- arid region --- brackish water --- sub surface drip irrigation (SDI) --- salinity --- sodicity --- olives trees --- excess precipitation --- irrigation water quality --- virtual water --- brackish groundwater --- rainwater harvesting --- socio-economic impacts

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Plant Adaptation to Global Climate Change discusses the issues of the impact of climate change factors (abiotic and biotic) on vegetation. This book also deals with simulation modeling approaches to understanding the long-term effects of different environmental factors on vegetation. This book is a valuable resource for the environmental science research community, including those interested in assessing climate change impacts on vegetation and researchers working on simulation modeling.

climate change impacts --- sugarcane --- yield --- harvested area --- production --- Thai agriculture --- rice --- heat stress --- whole genome DNA microarray --- yield loss --- MapMan analysis --- HRDE --- anthesis and maturity date --- crop yield --- SimCLIM --- DSSAT model --- planting date --- basal area increment --- air temperature --- precipitation --- Taylor’s power law --- tree ring analysis --- climate change --- farm work --- WBGT --- mitigation --- East Africa --- leaf temperature --- infrared thermography --- thermal imagery --- tropical rain forest --- isoprenoid exchanges --- ground --- litter emissions --- soil --- Pinus pinea --- distance gradient --- Mediterranean turf --- agro-ecosystems --- biodiversity --- weed communities --- Ethiopia highlands --- seasonal climate --- crop impacts --- bananas --- Black Sigatoka Leaf Disease --- climate --- global spread &amp --- impact --- habitat suitability --- species distribution --- conservation --- P. africana --- actual evapotranspiration --- modified Penman–Monteith --- sap flow --- scaling methods --- allometric correlations --- sapwood depth --- sapwood area --- leaf area index --- species distribution model --- scenarios --- GIS --- ecological niche --- grapevine --- n/a --- Taylor's power law --- modified Penman-Monteith

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Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible.

artificial neural network (ANN) --- Grain weevil identification --- neural modelling classification --- winter wheat --- grain --- artificial neural network --- ferulic acid --- deoxynivalenol --- nivalenol --- MLP network --- sensitivity analysis --- precision agriculture --- machine learning --- similarity --- metric --- memory --- deep learning --- plant growth --- dynamic response --- root zone temperature --- dynamic model --- NARX neural networks --- hydroponics --- vegetation indices --- UAV --- neural network --- corn plant density --- corn canopy cover --- yield prediction --- CLQ --- GA-BPNN --- GPP-driven spectral model --- rice phenology --- EBK --- correlation filter --- crop yield prediction --- hybrid feature extraction --- recursive feature elimination wrapper --- artificial neural networks --- big data --- classification --- high-throughput phenotyping --- modeling --- predicting --- time series forecasting --- soybean --- food production --- paddy rice mapping --- dynamic time warping --- LSTM --- weakly supervised learning --- cropland mapping --- apparent soil electrical conductivity (ECa) --- magnetic susceptibility (MS) --- EM38 --- neural networks --- Phoenix dactylifera L. --- Medjool dates --- image classification --- convolutional neural networks --- transfer learning --- average degree of coverage --- coverage unevenness coefficient --- optimization --- high-resolution imagery --- oil palm tree --- CNN --- Faster-RCNN --- image identification --- agroecology --- weeds --- yield gap --- environment --- health --- crop models --- soil and plant nutrition --- automated harvesting --- model application for sustainable agriculture --- remote sensing for agriculture --- decision supporting systems --- neural image analysis