The Digital Transformation of Row Crop Agriculture
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Digital Agribusiness Models”
Int.J.Curr.Microbiol.App.Sci (2020) 9(8): 3030-3038 International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 9 Number 8 (2020) Journal homepage: http://www.ijcmas.com Review Article https://doi.org/10.20546/ijcmas.2020.908.342 Innovations in Agriculture – A Case of “Digital Agribusiness Models” C. Muralidharan* Department of Social Sciences, Agricultural College and Research Insititute, Killikulam, Vallanad, Tuticorin District, India *Corresponding author ABSTRACT World population will increase at geometric rate of progression and an estimated 9.6 billion people and will require 70 percent more food than is available today. In order to increase the production efficiency, expertise in the domain of agriculture opined and K e yw or ds developed different agribusiness models to address the above aspects. In order to conduct this research, case study method was adopted to collect the necessary data and information Digital agriculture, from the entrepreneurs performing successful ventures in digital mode of agribusiness by Startups , Artificial adopting purposive sampling method. Secondary data pertaining to different countries Intelligence and were collected from the websites of digital agribusiness of their respective country. Digital Internet of Things agripreneurs enrolled in the Agribusiness incubation society were contacted personally to (IoT) collect the required data. M/s Kisangates Agro Informatics is an innovative startup with a motive to revolutionize business processes in agricultural ecosystem through strategic Article Info models and digital transformations that are synergistic with environment and business needs. Further, M/s Jeypee Biotechs, Virudhunagar developed the digital and ICT enabled Accepted: field advisory system model and executes networking among farmers on a common 24 July 2020 platform for dry chillies cultivation for export purposes. -
23 a CROP ROTATION PLANNING PROCEDURE Note
23 A CROP ROTATION PLANNING PROCEDURE Note: this chapter is modified from Chapter 5 of Crop Rotation on Organic Farms: a Planning Manual by Charles L. Mohler and Sue Ellen Johnson, NRAES Press. It is intended to be used in conjunction with that manual, not as a substitute for it. General instructions This chapter provides a step –by-step procedure for planning crop rotations on an individual farm. The procedure is based on methods used by the panel of expert farmers discussed in Chapter 2 supplemented with other sources. The procedure distills what experienced growers do based on experience, knowledge and intuition into a systematic method. For the sake of simplicity, the instructions are written as if the person doing the planning is the manager of the farm. The crop rotation planning procedure works through a series of steps. You will (i) organize your information, (ii) develop a general rotation plan (optional), (iii) construct a crop rotation planning map, (iv) plan future crop sequences for each section of the farm, and (v) refine your crop sequence plan. The procedure is easiest for a farm that produces only a few crops and has uniform field conditions. Examples include cash grain farms and some wholesale vegetable operations where all of the crops can be grown on all of the fields. The procedure can be used to plan rotations with more crops and multiple soil types, but the process is time consuming. The rewards of systematic crop rotation planning increase, however, with the number of crops and complexity of the fields. On farms that grow only a few crops, reasonable rotations can be maintained using a few rules of thumb. -
Agriculture 4.0 and Smart Sensors. the Scientific Evolution of Digital Agriculture: Challenges and Opportunities
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 31 May 2021 doi:10.20944/preprints202105.0758.v1 Article Agriculture 4.0 and Smart Sensors. The Scientific Evolution of Digital Agriculture: Challenges and Opportunities Michele Kremer Sott 1,*, Leandro da Silva Nascimento 2, Cristian Rogério Foguesatto 1, Leonardo B. Furstenau 2, Kadígia Faccin 1, Paulo Antônio Zawislak 2, Bruce Mellado 3, Jude Dzevela Kong 4 and Nicola Luigi Bragazzi 4,* 1 Unisinos University; [email protected] (M.K.S.), [email protected] (C.R.F.), [email protected] (K.F.) 2 Federal University of Rio Grande do Sul; [email protected] (L.B.F.), [email protected] (L.S.N.), [email protected] (P.A.Z.) 3 School of Physics and Institute for Collider Particle Physics, University of the Witwatersrand, Johannes- 7 burg, Wits 2050, South Africa; [email protected] 4 Department of Mathematics and Statistics, York University; [email protected] *Correspondence: [email protected] (M.K.S.); [email protected] (N.L.B.) Abstract: The agriculture sector is one of the backbones of many countries’ economies and its processes have been changing in order to enable technological adoption to increase productivity, quality, and sustainable development. In this research, we present a theoretical reflection through a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, the so-called Precision Agriculture (PA) and Agriculture 4.0 (A4.0). To do this, we used 4,694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis (BPNA) of the literature with the support of the PICOC protocol and the SciMAT software. -
Ecoregions of the Mississippi Alluvial Plain
92° 91° 90° 89° 88° Ecoregions of the Mississippi Alluvial Plain Cape Girardeau 73cc 72 io Ri Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of This level III and IV ecoregion map was compiled at a scale of 1:250,000 and depicts revisions and Literature Cited: PRINCIPAL AUTHORS: Shannen S. Chapman (Dynamac Corporation), Oh ver environmental resources; they are designed to serve as a spatial framework for the research, subdivisions of earlier level III ecoregions that were originally compiled at a smaller scale (USEPA Bailey, R.G., Avers, P.E., King, T., and McNab, W.H., eds., 1994, Omernik, J.M., 1987, Ecoregions of the conterminous United States (map Barbara A. Kleiss (USACE, ERDC -Waterways Experiment Station), James M. ILLINOIS assessment, management, and monitoring of ecosystems and ecosystem components. By recognizing 2003, Omernik, 1987). This poster is part of a collaborative effort primarily between USEPA Region Ecoregions and subregions of the United States (map) (supplementary supplement): Annals of the Association of American Geographers, v. 77, no. 1, Omernik, (USEPA, retired), Thomas L. Foti (Arkansas Natural Heritage p. 118-125, scale 1:7,500,000. 71 the spatial differences in the capacities and potentials of ecosystems, ecoregions stratify the VII, USEPA National Health and Environmental Effects Research Laboratory (Corvallis, Oregon), table of map unit descriptions compiled and edited by McNab, W.H., and Commission), and Elizabeth O. Murray (Arkansas Multi-Agency Wetland Bailey, R.G.): Washington, D.C., U.S. Department of Agriculture - Forest Planning Team). 37° environment by its probable response to disturbance (Bryce and others, 1999). -
Digital Agriculture and the Agridigit Project, Italy: Case Study Contribution to the OECD TIP Digital and Open Innovation Project
2 │ Digital Agriculture and the AgriDigit project, Italy: Case study contribution to the OECD TIP Digital and Open Innovation project Authors: Marcello Donatellia, Michele Pisantea,b a CREA, Council for Agricultural Research and Economics b University of Teramo Digital Agriculture and the Agridigit project, Italy – Contribution to the OECD TIP Digital and Open Innovation project │ 3 Table of contents Introduction ........................................................................................................................................... 4 1. Digital transformation of the agri-food sector ................................................................................ 6 2. The effects of digital transformation on innovation practices in the agri-food sector ................ 8 3. The AgriDigit project ...................................................................................................................... 10 References ............................................................................................................................................ 16 Digital Agriculture and the Agridigit project, Italy – Contribution to the OECD TIP Digital and Open Innovation project 4 │ Introduction The rapid development of innovation in the field of digital agriculture and agri-food chain, albeit in a chaotic frame of individual proposals, has highlighted an opportunity for a change of paradigm in establishing productivity in farms and of production areas. This has created a link between the production phase and product -
2021 Row Crop Plant-Back Intervals for Common Herbicides
DIVISION OF AGRICULTURE RESEARCH & EXTENSION University of Arkansas System Footnotes (continued) Authors 10 Replant only with Concep-treated or screen-treated seed. 2021 11 Needs 15 inches cumulative precipitation from application to planting rotational crop. Leah Collie, Program Associate - Weed Science 12 Needs 30 inches cumulative precipitation from application to planting rotational crop. Aaron Ross, Program Associate - Weed Science Tom Barber, Professor - Weed Science 13 Timeintervalisbasedon8oz/Aapplicationrateanddoesnotbeginuntil1inchof Row Crop Plant-Back rainfall is received. Tommy Butts, Assistant Professor - Weed Science 14If4oz/Aorlessusedand1inchofrainfall/irrigationreceivedafterapplication. Jason Norsworthy, Distinguished Professor - Weed Science 15 Days listed are based on University data and after receiving 1 inch of rainfall. 16 Enlist corn, cotton and soybeans can be planted immediately. University of Arkansas System, Division of Agriculture Intervals for 17 STS Soybeans can be planted immediately. Weed Science Program 18 Soil PH below 7.5. 19 ForNewpath/Prefaceuseratesgreaterthan8oz/Aperseason;onlysoybeansmaybe Common Herbicides planted the following year. 20 Rotation interval for soybean is 2 months where pH is less than 7.5. 21 Immediately if Poast Protected Crop. 22 If less than 15 inches of rainfall received since application, extend replant intervals to 18 months. If pH greater than 6.5, do not plant rice the following year. 23 18monthsforcottonifrateisgreaterthan5oz/AandpH>7.2. 24 Rotationtograinsorghumis18monthswhenSpartanisappliedat8oz/A. -
Section Five
SECTION FIVE irrigation Water ManageMent Planning Georgia’s agriculture industry utilizes millions of gallons of water each year for irrigation purposes. Ir- rigated agriculture ranges from row crop farming to plant nurseries to orchards. Much of the concern with irrigation is with regard to efficiency, runoff and the capture and collection of runoff. Many irrigation systems are inefficient and essentially waste water during the irrigating process. Ir- rigation efficiency may seem complicated; many producers may feel that their systems are simply too old to modify. In today’s agricultural industry, producers have numerous opportunities to improve the efficiency of their system in a cost efficient way. Improved nozzles, metering, and computer software are all readily available to provide producers with a more efficient means of irrigating their cropland and monitoring plant water use. Planning an irrigation schedule that best utilizes available water can reduce waste and runoff. Irrigation systems need to be routinely checked to ensure that water is being distributed uniformly and that there are no damaged pipes, sprinklers or nozzles. Irrigation meters provide producers with an accurate measurement of the amount of water that is applied to their crops and can also help identify pumping problems within their system. More information on the benefits of metering can be found in the UGA CES document, Water Meters as a Water Management Tool on Georgia Farms. An efficient system should range between 80 and 92% efficient. If a system is less than 80% efficient, producers should consider a system upgrade. There are several government sponsored irrigation system audit programs available and most system manufacturers can assist with an audit program. -
ITU-FAO Stocktaking Report: Digital Excellence in Agriculture in Europe and Central Asia
Digital Excellence in Agriculture in Europe and Central Asia Good practices in the field of digital agriculture Stocktaking Report @ITU and @FAO | July 2021 -Living document- Please note that the content of this document was provided by the submitter and ITU and FAO are not accountable for the information displayed. 2 ACKNOWLEDGMENTS This report was developed by the ITU Office for Europe within the framework of the ITU Regional Initiative for Europe on information and communication technology-centric innovation ecosystems. It was elaborated by Mr Laszlo Papocsi and Mr Mihaly Csoto, with the support of Ms Daniela DiGianantonio and Mr Eugen Harabara. ITU and FAO would also like to acknowledge the contribution of Mr Rinor Ahmeti (FAO), Ms Sarah Delporte (ITU), Mr Victor Lagutov (FAO), Mr Thembani Malapela (FAO), Mr Valentin Nagy (FAO), Mr Farid Nakhli (ITU), Mr Joshua Oiro (ITU), and Ms Antonia Vanzini (ITU). The work has been undertaken under the supervision and direction of Mr Jaroslaw Ponder, Head of ITU Office for Europe, and Ms Sophie Treinen, Information and Knowledge Management Officer and Digital Agriculture Team Leader of FAO Regional Office for Europe and Central Asia. FAO and ITU would also like to express sincere gratitude to all the participants of the Regional Contest “Digital Excellence in Agriculture in Europe and Central Asia” for their valuable submissions. Please note that the content of this document was provided by the submitter and ITU and FAO are not accountable for the information displayed. 3 DISCLAIMER ITU and FAO. 2021. Digital Excellence in Agriculture in Europe and Central Asia - Call for good practices in the field of digital agriculture - Stocktaking Report. -
The Implications of Digital Agriculture and Big Data for Australian Agriculture
research report April 2016 The Implications of Digital Agriculture and Big Data for Australian Agriculture © 2016 Australian Farm Institute ISBN 978-1-921808-38-8 (Print and Web) Australia’s Independent Farm Policy Research Institute The Australian Farm Institute The Australian Farm Institute is an agricultural policy research organisation that has been established to develop and promote public policies that maximise the opportunity for Australian farmers to operate their businesses in a profitable and sustainable manner. To do this, the Institute carries out or contracts leading academics and consultants to conduct research into farm policy issues that the Institute’s Research Advisory Committee has identified as being of high strategic importance for Australian farmers. The Institute has a commitment to ensuring research findings are the conclusion of high quality, rigorous and objective analysis. The Australian Farm Institute promotes the outcomes of the research to policy-makers and the wider community. The Australian Farm Institute Limited is incorporated as a company limited by guarantee and commenced operations on 23 March 2004. The Institute is governed by a Board of Directors who determine the strategic direction for the Institute. The Institute utilises funding voluntarily contributed by individuals and corporations to perform its activities. Initial seed funding has been contributed by the NSW Farmers’ Association. Vision Farm policies that maximise the opportunity for Australian farmers to operate their businesses in a profitable and sustainable manner. Objective To enhance the economic and social wellbeing of farmers and the agricultural sector in Australia by conducting highly credible public policy research, and promoting the outcomes to policy-makers and the wider community. -
Urban Bites and Agrarian Bytes: Digital Agriculture and Extended Urbanization
Berkeley Planning Journal 31 100 Urban Bites and Agrarian Bytes: Digital Agriculture and Extended Urbanization TIMOTHY RAVIS AND BENJAMIN NOTKIN Abstract Capitalist agriculture faces a crisis. Plateauing yields and profts are driving up food prices, and the ability to continue the traditional practice of expanding into new, un-commodifed territories appears to be waning. This crisis is due in large part to the accelerating biophysical contradictions of industrial agriculture, which systematically undermine the ecological conditions for its own success in pursuit of proft. We investigate how digital technologies are deployed as a potential data fx that does not solve the crisis but merely staves it of. We situate these technologies within the material context of capitalist urbanization, along the way arguing for bringing information back into the neo-Lefebvrian framework of “extended” or “planetary” urbanization. Digital agriculture technologies continue the centralization of economic knowledge and power as they facilitate the transformation of vast territories into “operational landscapes” that provide the material, energy, and labor for a rapidly expanding urban system. Keywords: Digital Agriculture, Precision Agriculture, Extended Urbanization, Planetary Urbanization, Globalization, Agrarian Studies, Depeasantization, Globalization, Computation “Eventually, precision agriculture could take humans out of the loop entirely. Once that happens, the world won’t just see huge gains in productivity. It will see a fundamental shif in the history of agriculture: farming without farmers.” —Foreign Afairs Magazine (Lowenberg-DeBoer 2015) “99% of all technological disruption is there to merely ensure that nothing of substance gets disrupted at all.” —Evgeny Morozov (2019) Introduction: Feeding “the Next Two Billion” Hundreds of reports and articles begin with a variation on the same apocalyptic exhor- tation: The combination of population growth, food price volatility, and climate change demands a new agricultural revolution to expand and secure the global food supply. -
Value Creation in the Digital Agribusiness Network Transform and Grow to Sustainably Feed the World
Value Creation in the Digital Agribusiness Network Transform and Grow to sustainably feed the world - 1 - SAP Digital Agribusiness Whitepaper (03/16) © 2016 SAP SE. All rights reserved ANJA’S POINT OF VIEW Dear Customers, The world population is growing to upwards of 10 billion people by the middle of the century.1 This growth combined with urbanization and the rise of the middle class will increase the demand for healthy, fairly produced, and sustainable food and will require agricultural production to double.2 To succeed, we need smart solutions from farm to fork. We expect the digitization of agribusiness to play a key role in solving this challenge. New processes and technologies optimize seed selection, irrigation, fertilization, and crop protection; automate farming work with autonomous equipment; optimize asset utilization; and streamline the food supply chain to avoid waste. But even in a high-tech economy, agriculture is still exposed to the weather, crop and animal diseases, and substantial fluctuations on commodity markets. Predictive analytics and simulations enable optimized risk mitigation strategies. Digital technology is turning the farm into a digital enterprise and the farmer into a digital entrepreneur. Farmers stand in the center of a complex ecosystem of farming equipment manufacturers, food "Digital technology and processors, and agrichemical specialists. At the same time, consumer behavior is changing collaboration are key radically. Consumers are the focus point of the food industry. They want to know the origin of their food and how it was produced and processed, driving the need for transparency along the enablers to provide end-to-end agribusiness supply chain. -
From Smart Farming Towards Agriculture 5.0: a Review on Crop Data Management
agronomy Review From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management Verónica Saiz-Rubio * and Francisco Rovira-Más Agricultural Robotics Laboratory (ARL), Universitat Politècnica de València, Camino de Vera, s/n. 46022 Valencia, Spain; [email protected] * Correspondence: [email protected]; Tel.: +34-963-877-291 Received: 2 December 2019; Accepted: 17 January 2020; Published: 3 February 2020 Abstract: The information that crops offer is turned into profitable decisions only when efficiently managed. Current advances in data management are making Smart Farming grow exponentially as data have become the key element in modern agriculture to help producers with critical decision-making. Valuable advantages appear with objective information acquired through sensors with the aim of maximizing productivity and sustainability. This kind of data-based managed farms rely on data that can increase efficiency by avoiding the misuse of resources and the pollution of the environment. Data-driven agriculture, with the help of robotic solutions incorporating artificial intelligent techniques, sets the grounds for the sustainable agriculture of the future. This paper reviews the current status of advanced farm management systems by revisiting each crucial step, from data acquisition in crop fields to variable rate applications, so that growers can make optimized decisions to save money while protecting the environment and transforming how food will be produced to sustainably match the forthcoming population growth. Keywords: agriculture 4.0; big data; farm management information system (FMIS); robotics; IoT; variable-rate technology (VRT); AI 1. Introduction The agriculture sector is undergoing a transformation driven by new technologies, which seems very promising as it will enable this primary sector to move to the next level of farm productivity and profitability [1].