Pelaksanaan Pengadaan Tanah Pada Pembangunan Jaringan Irigasi Daerah Irigasi (DI) Kawasan Sawah Laweh di Kabupaten Pesisir Selatan

Penelitian ini dilatarbelakangi oleh pelaksanaaan pengadaan tanah pada pembangunan jaringan irigasi Daerah Irigasi Kawasan Sawah Laweh yang tidak terlepas dari kendala-kendala dalam pelaksanaannya seperti masyarakat yang tidak setuju dengan harga ganti rugi, terjadinya tumpang tindih kepemilikan lahan sesama kaum yang biasa terjadi antara mamak dan kemenakan, serta terbatasnya anggaran ganti kerugian bagi pihak yang sudah mau membebaskan lahannya. Penelitian ini bertujuan guna menganalisis pelaksanaan pengadaan tanah sesuai dengan ketentuan undang-undang pengadaan tanah yaitu Undang-Undang No. 2 Tahun 2012. Metode penelitian ini yakni metode quasi kualitatif dengan desain simple research, pengumpulan data melalui wawancara, observasi, serta dokumentasi. Hasil penelitian menunjukkan bahwa pengadaan tanah untuk pembangunan jaringan irigasi Daerah Irigasi (DI) Kawasan Sawah Laweh sudah dilaksanakan sesuai dengan undang-undang pengadaan tanah yaitu Undang-Undang No.2 Tahun 2012. Kendala yang terjadi sudah diatasi oleh Pemerintah Kabupaten Pesisir Selatan untuk dicari solusinya

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

We thank the China Scholarship Council (CSC) for providing a scholarship (202206710073) to Zewei Jiang. This work was supported by the Fundamental Research Funds for the Central Universities (B220203009), the Postgraduate Research & Practice Program of Jiangsu Province (KYCX22_0669), the Water Conservancy Science and Technology Project of Jiangxi Province (201921ZDKT06, 202124ZDKT09), the National Natural Science Foundation of China (51879076), the Fundamental Research Funds for the Central Universities (B210204016), Science & Technology Specific Projects in Agricultural High-tech Industrial Demonstration Area of the Yellow River Delta, Grant No: 2022SZX01.Peer reviewedPublisher PD

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule

Deep learning sensor fusion in plant water stress assessment: A comprehensive review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations

Predição para o uso da inteligência artificial no agronegócio na Caatinga

A ciência e a tecnologia, em diferentes formas, sempre exerceram um papel expressivo na solução de problemas, sendo usadas para o desenvolvimento de estratégias, produtos, métodos e ferramentas. Os avanços em ciência e tecnologia têm se mostrado promissores no intuito de aprimorar setores como o agronegócio. E essa visão tem sido justificada pelo constante avanço de dispositivos tecnológicos projetados para apresentar soluções aos problemas agrícolas. Sendo assim, este estudo tem por objetivo analisar o processo de inovação no contexto da Inteligência Artificial (IA), desde a produção do conhecimento científico até a fase de predição dessa tecnologia no agronegócio na Caatinga. Do ponto de vista dos aspectos metodológicos a pesquisa é classificada como exploratória, uma vez que essa investigação leva em consideração uma área na qual há pouco conhecimento acumulado e sistematizado. Em relação à técnica de pesquisa, é caracterizada como estudo de caso. Os resultados da aplicação dos métodos da IA no agronegócio no contexto geral apresentam diferentes abordagens como o uso de Visão de Máquina por meio de Sistema Agrícola Virtual, SVM e ELM na detecção precoce do patógeno de pragas e doenças; FIS e MLP para a exploração de culturas; propagação reversa para monitoramento dos limites da fazenda; ANN e MFNN para análise de estruturas de irrigação; e Árvore da Decisão e TDNN para a vigilância do rebanho. Com os dispositivos integrados no sistema de produção agrícola os sistemas das fazendas passam a oferecer recomendações e insights mais ricos para a tomada de decisão e melhoria da cadeia de suprimentos agrícola. Em relação ao levantamento das tecnologias atuais no agronegócio na Caatinga, o contexto local apresenta abordagens bem distintas, desde a utilização de técnicas de convivência com o semiárido como os métodos de manejo do solo, aproveitamento da água da chuva e preparo de ração animal. Já a análise do uso das tecnologias, o enfoco está na viabilidade da produção, diversificação e manejo da colheita em polos integrados de grande desenvolvimento tecnológico em polos de cultivo e manejo de culturas irrigadas. A perspectiva da adoção e o desenvolvimento de IA no agronegócio na Caatinga ainda se encontram em fase inicial, com os agentes buscando nas pesquisas, conhecer as oportunidades dessa tecnologia frente aos negócios no setor agrícola. Na Caatinga, os estudos ainda são reduzidos, mas já há exemplos como rastreabilidade de carne, predição da produtividade da palma forrageira, delineamento de zonas de manejo ou mesmo na estimativa da evapotranspiração de referência. Contudo, há etapas que devem ser superadas até a integração da IA como a habilidade de entender e manusear as ferramentas com IA e a integração dos sistemas dentro da cadeia de suprimentos. Já os resultados do levantamento sistemático apresentam ações como modelagem e previsão do fluxo de água; evapotranspiração; variabilidade, avaliação de terra; previsão de época ótima de semeadura e seleção de cultivares. De modo que, os achados apresentam os diferentes usos da IA, com iniciativas de sustentabilidade habilitadas por mudanças no sistema agrícola atual.Science and technology, in different forms, have always played an expressive role in problem solving, being used for the development of strategies, products, methods and tools. Advances in science and technology have shown promise in order to improve sectors such as agribusiness. And this vision has been justified by the constant advancement of technological devices designed to present solutions to agricultural problems. Therefore, this study aims to analyze the innovation process in the context of artificial intelligence, from the production of scientific knowledge to the prediction phase of this technology in agribusiness in the Caatinga. From the point of view of methodological aspects, the research is classified as exploratory, since this investigation takes into account an area in which there is little accumulated and systematized knowledge. Regarding the research technique, it is characterized as a case study. The results of the application of AI methods in agribusiness in the general context present different approaches such as the use of Machine Vision through Virtual Agricultural System, SVM and ELM in the early detection of the pathogen of pests and diseases; FIS and MLP for the exploitation of cultures; reverse propagation for monitoring farm boundaries; ANN and MFNN for analysis of irrigation structures; and Decision Tree and TDNN for herd surveillance. With the devices integrated into the agricultural production system. farm systems now offer richer recommendations and insights for decision making and agricultural supply chain improvement. Regarding the survey of current technologies in agribusiness in the Caatinga, the local context presents very different approaches, from the use of technologies of coexistence with the semi-arid region or social techniques such as methods of soil management, use of rainwater and preparation of feed animal. Even the use of technologies themselves aimed at the viability of production, diversification and management of the harvest in integrated poles of great technological development in poles of cultivation and management of irrigated cultures. The perspective of the adoption and development of AI in agribusiness in the Caatinga is still at an early stage, with agents seeking, in research, to know the opportunities of this technology in relation to business in the agricultural sector. In the Caatinga, studies are still very limited, but there are already examples such as meat traceability, prediction of forage cactus productivity, delineation of management zones or even in the estimation of reference evapotranspiration. However, there are steps that must be overcome until the integration of AI such as the ability to understand and handle the tools with AI and the integration of systems within the supply chain. On the other hand, the results of the systematic survey present actions such as modeling and forecasting the water flow; evapotranspiration; variability, land assessment; prediction of optimal sowing time and selection of cultivars. So, the findings present the different uses of AI, with sustainability initiatives enabled by changes in the current agricultural system

Conserving land, protecting water

Water resource management / Water productivity / Water conservation / Recycling / Land management / Soil conservation / Ecosystems / Ecology / Evapotranspiration / Food security / Poverty / River basins / Irrigated farming

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Evaluation of floodwater spreading for groundwater recharge in Gareh Bygone Plain, southern Iran

The overall objective of this dissertation was to evaluate a floodwater spreading system that is installed in 1981 at the Gareh Bygone Plain, southern Iran for recharging the groundwater table. As the spatial and temporal distribution of the evapotranspiration (ET) was a necessary input, an energy balance model “SEBS” was calibrated for the study area and its results were cross checked with water budget results to obtain the reliable ET maps. TDR method was then evaluated for the original stony soils and a set of new coefficients were generated for these soils and the new equations for cable length correction for accurately converting the soil permittivity to ɵv. Two methods of recharge assessment “saturated zone” and “vadose zone” were further employed to determine the ratio between total and artificial recharge independently. In saturated zone, water table fluctuation and water budget concepts were combined and the effect of flooding event on groundwater table was substantiated and the recharge was assessed for a selected hydrological year. In vadose zone, three experimental well were installed with the depth of ~30m and one was equipped with the calibrated TDR probes and the time series of soil-water data were collected for the three years successively. Soil water budget method and a modelling approach by Hydrus 1d was used independently to simulate the water movement and assess the recharge after a flooding event. Calibration of the H1D model by inverse solution resulted in RMSE values of simulated vs. observed ɵv of 0.02 to 0.05 (m3 m-3) for different subsurface layers. Calculations, indicated that out the 51.8 cm of ponded floodwater during the 16 January to 23 August 2011 period, 29.6 cm of cumulative recharge occurred, showing an efficiency of 57%. Two independent approaches suggest that 57 to 61% of input water effectively flows to the groundwater table. The optimized hydraulic parameters of the representative layers in aquifer profile, can be applied in future studies when attempting to up-scale our findings

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue