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| Filter results9 paper(s) found. |
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1. Beyond the Mean: A Quantile Count Regression Analysis of Precision Farming Technology Adoption Intensity by German FarmersThis study examines the factors influencing precision agriculture technology adoption intensity among German farmers using an innovative quantile count regression approach that reveals heterogeneous relationships across different segments of the adoption distribution. While previous research has primarily relied on mean-based regression models that may mask important variation in adoption determinants, this analysis provides new insights into how factors affect low, moderate, and high technol... M. Michels, O. Musshoff |
2. Worldwide Crop Precision Agriculture Adoption: 2026 UpdateRobotics, machine vision, and drone spraying have attracted much attention in recent years. But the technologies introduced in the 2000s and earlier, such as yield monitors, guidance, variable rate technology (VRT), and digital imagery continue to advance worldwide, more on large mechanized grain and oilseed farms. This update summarizes census estimates and adoption surveys with statistically reliable random sampling from 24 countries. Within-country and farm size breakdowns are reported whe... B. Erickson, J. Mcfadden, E. Morimoto, J. Lowenberg-deboer |
3. Expanding Access to High-Resolution Soil pH Measurement: From Smallholder Farms to 200-Hectare FieldsSoil pH is one of the most influential and correctable soil properties affecting crop productivity. It governs nutrient availability, toxicity risk, microbial activity, and overall soil function. Yet timely and spatially representative pH measurement remains inconsistent across agricultural systems due to laboratory turnaround times, sampling costs, limited field access, and sub-field variability not captured by conventional sampling density. This study evaluated multiple strategies... T. Lund, C. Maxton, E. Lund |
4. Benchmarking Precision Agriculture Adoption in the United States and BrazilThe United States has long been regarded as a global leader in agricultural technology and productivity. However, rapid advancements in other major producing countries are challenging this position. Brazil, in particular, has paired large-scale crop expansion with accelerated digital transformation, raising important questions about where the United States continues to lead and where it risks losing its competitive edge. Understanding how precision agriculture technologies are being adopted a... J. Colussi, B. Erickson, T. Malone, M. Langemeier, C. Fiechter |
5. Understanding Adoption and Post-Adoption Impacts of Smart Farming Technologies in ItalySmart farming technologies (SFTs) are increasingly promoted as key enablers of agricultural efficiency, resource optimization, and environmental sustainability. However, despite rapid technological advancement, empirical evidence on realized economic and resource-use impacts under real farming conditions remains limited, creating uncertainty about the magnitude and distribution of impacts. Existing evidence remains largely focused on perceived drivers, barriers, and intentions to adopt, rathe... |
6. Accuracy Analysis of C/A Code-based GNSS Receivers in Kinematic ConditionPrecision agriculture has emerged as a strategic approach to optimize input use and maximize crop productivity. One of the key pillars of this practice is the collection of georeferenced data, essential for the monitoring and efficient management of cultivated areas. This report aims to compare the performance of different positioning signal reception technologies under dynamic conditions. Three C/A code navigation receivers integrated into smartphones, one conventional navigation GPS receive... S. Ribeiro, G. Sanches, C.N. Ricci, J. Regazzo, J.P. Molin |
7. Digital Agriculture in Dairy Farming: Connectivity Diagnosis and Barriers to Technology Adoption in an Agrotechnological DistrictThe integration of digital tools and communication systems has underpinned a profound transformation in global production chains, aiming to optimize farm operations through technological innovation. Within this context, the present study forms part of the Semear Digital project, coordinated by Embrapa, and is grounded in the premise that digital inclusion constitutes an indispensable strategy for the sustainability and competitiveness of contemporary dairy farming. The primary object... M.D. Melo, C.M. Paiva, A.L. Oliveira, F.N. Maciel, G.C. Siqueira , M.R. Borges , G.S. Furtado, P.M. Leme |
8. Impact of Sampling Density on the Spatial Prediction of Soil Chemical Attributes Using Geostatistics and Machine LearningSoil sampling at high grid densities represents a significant economic barrier to the adoption of Precision Agriculture (PA) in Brazil. This study evaluates the trade-off between sampling density and interpolation quality by comparing geostatistical methods and machine learning algorithms. Three distinct approaches were statistically assessed: Ordinary Kriging (OK), Random Forest (RF), and the hybrid Random Forest Regression Kriging (RFRK). The analysis was conducted across six fields totalin... S. Ribeiro, H. Fantin gebler, J.P. Molin, R.F. Da silva |
9. Determinants of the Intensity of Digital Precision Technology Adoption in Brazilian FeedlotsPrecision livestock farming has gained prominence as a tool to enhance managerial control and reduce risk in intensive production systems. In the case of Brazilian beef cattle feedlots, characterized by high price volatility, tight margins, and increasing pressure for environmental performance, the adoption of digital technologies represents a relevant strategy to improve decision-making processes. Unlike studies that focus solely on binary adoption (adopt/non-adopt), understanding adoption i... G. , M.J. Carrer, M. , L.C. David, H.M. Souza filho, E. Bonjour |