Climate Neutral and Resilient Farming Systems

This book presents evidence-based research on climate-neutral and resilient farming systems and further to provide innovative and practical solutions for reducing greenhouse gas emissions and mitigating the impact of climate change. Intensive farming systems are a significant source of greenhouse gas emissions, thereby contributing to global warming and the acceleration of climate change. As paddy rice farming is one of the largest contributors, and most environmentally damaging farming systems, this will be a particular focus of the book. The mitigation of greenhouse gas emissions needs to be urgently addressed to achieve the 2 degrees Celsius target adopted by COP21 and the 2015 Paris Agreement, but this is not possible if local and national level innovations are not accompanied by international level cooperation, mutual learning and sharing of knowledge and technologies. This book, therefore, brings together international collaborative research on climate-neutral and resilient farming systems compiled by leading scientists and experts from Europe, Asia and Africa. The chapters present evidence-based research and innovative solutions that can be applied or upscaled in different farming systems and regions across the world. Chapters present models and technologies that can be used for practical implementation at the systemic level and advance state of the art knowledge on carbon neutral farming. Combining theory and practice, this interdisciplinary book provides guidance which can inform and increase cooperation between researchers from various countries on climate-neutral and resilient farming systems. Most importantly, the volume provides recommendations which can be put into practice by those working in the agricultural industry, especially in developing countries, where they are attempting to promote climate-neutral and resilient farming systems. The book will be of great interest to students and academics of sustainable agriculture, food security, climate mitigation and sustainable development, in addition to policymakers and practitioners working in these areas

Climate Neutral and Resilient Farming Systems

This book presents evidence-based research on climate-neutral and resilient farming systems and further to provide innovative and practical solutions for reducing greenhouse gas emissions and mitigating the impact of climate change. Intensive farming systems are a significant source of greenhouse gas emissions, thereby contributing to global warming and the acceleration of climate change. As paddy rice farming is one of the largest contributors, and most environmentally damaging farming systems, this will be a particular focus of the book. The mitigation of greenhouse gas emissions needs to be urgently addressed to achieve the 2 degrees Celsius target adopted by COP21 and the 2015 Paris Agreement, but this is not possible if local and national level innovations are not accompanied by international level cooperation, mutual learning and sharing of knowledge and technologies. This book, therefore, brings together international collaborative research on climate-neutral and resilient farming systems compiled by leading scientists and experts from Europe, Asia and Africa. The chapters present evidence-based research and innovative solutions that can be applied or upscaled in different farming systems and regions across the world. Chapters present models and technologies that can be used for practical implementation at the systemic level and advance state of the art knowledge on carbon neutral farming. Combining theory and practice, this interdisciplinary book provides guidance which can inform and increase cooperation between researchers from various countries on climate-neutral and resilient farming systems. Most importantly, the volume provides recommendations which can be put into practice by those working in the agricultural industry, especially in developing countries, where they are attempting to promote climate-neutral and resilient farming systems. The book will be of great interest to students and academics of sustainable agriculture, food security, climate mitigation and sustainable development, in addition to policymakers and practitioners working in these areas

Prediction of the Nitrogen Content of Rice Leaf Using Multi-Spectral Images Based on Hybrid Radial Basis Function Neural Network and Partial Least-Squares Regression

This paper’s novel focus is predicting the leaf nitrogen content of rice during growing and maturing. A multispectral image processing-based prediction model of the Radial Basis Function Neural Network (RBFNN) model was proposed. Moreover, this paper depicted three primary points as the following: First, collect images of rice leaves (RL) from a controlled condition experimental laboratory and new shoot leaves in different stages in the visible light spectrum, and apply digital image processing technology to extract the color characteristics of RL and the morphological characteristics of the new shoot leaves. Secondly, the RBFNN model, the General Regression Model (GRL), and the General Regression Method (GRM) model were constructed based on the extracted image feature parameters and the nitrogen content of rice leaves. Third, the RBFNN is optimized by and Partial Least-Squares Regression (RBFNN-PLSR) model. Finally, the validation results show that the nitrogen content prediction models at growing and mature stages that the mean absolute error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) of the RFBNN model during the rice-growing stage and the mature stage are 0.6418 (%), 0.5399 (%), 0.0652 (%), and 0.3540 (%), 0.1566 (%), 0.0214 (%) respectively, the predicted value of the model fits well with the actual value. Finally, the model may be used to give the best foundation for achieving exact fertilization control by continuously monitoring the nitrogen nutrition status of rice. In addition, at the growing stage, the RBFNN model shows better results compared to both GRL and GRM, in which MAE is reduced by 0.2233% and 0.2785%, respectively

Remote Sensing for Precision Nitrogen Management

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

Assessment of maize nitrogen uptake from PRISMA hyperspectral data through hybrid modelling

The spaceborne imaging spectroscopy mission PRecursore IperSpettrale della Missione Applicativa (PRISMA), launched on 22 March 2019 by the Italian Space Agency, opens new opportunities in many scientific domains, including precision farming and sustainable agriculture. This new Earth Observation (EO) data stream requires new-generation approaches for the estimation of important biophysical crop variables (BVs). In this framework, this study evaluated a hybrid approach, combining the radiative transfer model PROSAIL-PRO and several machine learning (ML) regression algorithms, for the retrieval of canopy chlorophyll content (CCC) and canopy nitrogen content (CNC) from synthetic PRISMA data. PRISMA-like data were simulated from two images acquired by the airborne sensor HyPlant, during a campaign performed in Grosseto (Italy) in 2018. CCC and CNC estimations, assessed from the best performing ML algorithms, were used to define two relations with plant nitrogen uptake (PNU). CNC proved to be slightly more correlated to PNU than CCC (R-2 = 0.82 and R-2 = 0.80, respectively). The CNC-PNU model was then applied to actual PRISMA images acquired in 2020. The results showed that the estimated PNU values are within the expected ranges, and the temporal trends are compatible with plant phenology stages

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Indian Agriculture Towards 2030

This open access book brings together varying perspectives for transformational change needed in India’s agriculture and allied sectors. Stressing the need of thinking for a post-Green Revolution future, the book promotes approaching this change through eight broad areas, indicating the policy shifts needed to meet the challenges for the coming decade (2021-2030). The book comprises of ten contributions. Apart from the overview chapter on transformational change and the concluding chapter on pathways for 2030, there are eight thematic chapters on topics such as transforming Indian agriculture, dietary diversity for nutritive and safe food; climate crisis and risk management; water in agriculture; pests, pandemics, preparedness and biosecurity natural farming; agroecology and biodiverse futures; science, technology and innovation in agriculture; and structural reforms and governance. The writing style of these papers written by technical experts is forward-looking—not merely an analysis of what has been and why it was so, but what ought to be. This is an essential reading for those interested in agriculture, food and nutrition sectors of India, and more so their interconnectedness