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This book contains 17 chapters providing details on decision tools that use linear programming to determine recommendations specific to a farmer's context intended to maximize profit from fertilizer use in sub-Saharan Africa. Chapter 1 and 2 discusses the principles and approach, and spatial analysis of fertilizer use optimization, respectively. Chapter 3 covers integrated soil fertility management in sub-Saharan Africa. Further, Chapters 4 to 16 explore optimizing fertilizer use within an integrated soil fertility management framework in countries in sub-Saharan Africa, including: Burkina Faso, Ethiopia, Ghana, Kenya, Mali, Malawi, Mozambique, Niger, Nigeria, Rwanda, Tanzania, Uganda, and Zambia. Finally, Chapter 17 describes the process of enabling fertilizer use optimization in sub-Saharan Africa.

Fertilizers --- Soil fertility --- Application --- Soils --- Fallowing --- Soil productivity --- Artificial fertilizers --- Chemical fertilizers --- Crops --- Fertilisers --- Fertilization of soils --- Fertilizers and manures --- Field crops --- Agricultural chemicals --- Plants --- Soil amendments --- Fertilization --- Fertilizer movement --- Nutrition --- profitability --- Nyasaland --- Least Developed Countries --- fertilisers --- linear programming --- application rates --- Mozambique --- optimization --- Malawi --- Mali --- ACP Countries --- Nigeria --- Abyssinia --- Community of Portuguese Language Countries --- profits --- Commonwealth of Nations --- fertilizers --- Bourkina Fasso --- Niger --- Rwanda --- Tanzania --- Africa --- Ghana --- Zambia --- soil fertility --- Kenya --- Tanganyika --- Portuguese Speaking Africa --- Africa South of Sahara --- Burkina Faso --- West Africa --- SADC Countries --- Southern Africa --- Francophone Africa --- subsaharan Africa --- Uganda --- Developing Countries --- Anglophone Africa --- Ethiopia --- East Africa

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This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment.

Technology: general issues --- History of engineering & technology --- Environmental science, engineering & technology --- UAS --- multiple sensors --- vegetation index --- leaf nitrogen accumulation --- plant nitrogen accumulation --- pasture quality --- airborne hyperspectral imaging --- random forest regression --- sun-induced chlorophyll fluorescence (SIF) --- SIF yield indices --- upward --- downward --- leaf nitrogen concentration (LNC) --- wheat (Triticum aestivum L.) --- laser-induced fluorescence --- leaf nitrogen concentration --- back-propagation neural network --- principal component analysis --- fluorescence characteristics --- canopy nitrogen density --- radiative transfer model --- hyperspectral --- winter wheat --- flooded rice --- pig slurry --- aerial remote sensing --- vegetation indices --- N recommendation approach --- Mediterranean conditions --- nitrogen --- vertical distribution --- plant geometry --- remote sensing --- maize --- UAV --- multispectral imagery --- LNC --- non-parametric regression --- red-edge --- NDRE --- dynamic change model --- sigmoid curve --- grain yield prediction --- leaf chlorophyll content --- red-edge reflectance --- spectral index --- precision N fertilization --- chlorophyll meter --- NDVI --- NNI --- canopy reflectance sensing --- N mineralization --- farmyard manures --- Triticum aestivum --- discrete wavelet transform --- partial least squares --- hyper-spectra --- rice --- nitrogen management --- reflectance index --- multiple variable linear regression --- Lasso model --- Multiplex®3 sensor --- nitrogen balance index --- nitrogen nutrition index --- nitrogen status diagnosis --- precision nitrogen management --- terrestrial laser scanning --- spectrometer --- plant height --- biomass --- nitrogen concentration --- precision agriculture --- unmanned aerial vehicle (UAV) --- digital camera --- leaf chlorophyll concentration --- portable chlorophyll meter --- crop --- PROSPECT-D --- sensitivity analysis --- UAV multispectral imagery --- spectral vegetation indices --- machine learning --- plant nutrition --- canopy spectrum --- non-destructive nitrogen status diagnosis --- drone --- multispectral camera --- SPAD --- smartphone photography --- fixed-wing UAV remote sensing --- random forest --- canopy reflectance --- crop N status --- Capsicum annuum --- proximal optical sensors --- Dualex sensor --- leaf position --- proximal sensing --- cross-validation --- feature selection --- hyperparameter tuning --- image processing --- image segmentation --- nitrogen fertilizer recommendation --- supervised regression --- RapidSCAN sensor --- nitrogen recommendation algorithm --- in-season nitrogen management --- nitrogen use efficiency --- yield potential --- yield responsiveness --- standard normal variate (SNV) --- continuous wavelet transform (CWT) --- wavelet features optimization --- competitive adaptive reweighted sampling (CARS) --- partial least square (PLS) --- grapevine --- hyperparameter optimization --- multispectral imaging --- precision viticulture --- RGB --- multispectral --- coverage adjusted spectral index --- vegetation coverage --- random frog algorithm --- active canopy sensing --- integrated sensing system --- discrete NIR spectral band data --- soil total nitrogen concentration --- moisture absorption correction index --- particle size correction index --- coupled elimination