Shared digital agricultural technology on farms in Southern Germany-analysing farm and socio-demographic characteristics in an inter-farm context

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Michael Gscheidle, Thies Petersen, Reiner Doluschitz
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引用次数: 0

Abstract

Introduction

Up till now, digitalisation in agriculture has almost only been discussed in the context of large farms. However, sooner or later, ongoing digitalisation will reach the agricultural sector as a whole. Indeed, even smaller farms can also benefit from the opportunity and make profitable use of digital agricultural technology by adopting inter-farm organisational forms e.g. collaboration between farmers or contractor services. This article seeks to gain a better understanding of the digital transformation process and to validate relevant forecasts by analysing farm and socio-demographic characteristics that have a possible influence on the likelihood of inter-farm use of digital agricultural technology in general and regardless of the organisational form.

Methodological approach

Univariate analysis approaches and bivariate analysis approaches were selected to describe the sample. A binary regression analysis was used to analyse the results of a written online survey of farmers from southern Germany. The characteristics listed in hypotheses H1 to H10 serve as a theory-based conceptual framework for the statistical analysis within the binary logistic regression model.

Results

The results of this study are based on a survey sample of 165 farmers, 36.4 % (n=60) of whom use digital agricultural technology on an inter-farm basis. The sample covers n=89 farms from Baden-Württemberg and n=76 from Bavaria. Most of the farmers (87.3 %) considered themselves perfectly capable of using digital technologies confidently after it had been explained to them once (x̅=2.52, s=1.02, scale: 1=completely true to 6=not true at all), with 38.2 % of them using digital agricultural technology across farms, that means they use digital agricultural technology together. Certain factors which can influence the likelihood of inter-farm use of digital agricultural technology in small-scale regions were identified using the binary logistic regression model to analyse the relevant operational and socio-demographic characteristics. Using this methodological approach, eight predictors were identified, three of which have a positive influence on the likelihood of inter-farm use of digital agricultural technology: the availability of two external labourers, the farm's focus on “finishing” or on “other” activities such as taking horses at livery or fattening livestock. Farms that have less than 200 hectares, have no clear succession plan, or whose farm managers are under 30 years old are less likely to use inter-farm digital agricultural technology.

Conclusions

In this study, several influencing factors were identified that can play a role in the shared use of digital agricultural technology, especially between farmers in small-scale regions in southern Germany. The empirical results obtained from the binary logistic regression show both positive and negative influences on the likelihood of inter-farm use of digital agricultural technology. Forms of cooperation between farmers play a central role in the establishment and use of capital-intensive digital agricultural systems on farms in southern Germany. The study therefore emphasises that the widespread and economical use of digital agricultural technology in small-scale regions can be achieved quickly, especially through established collaborations between farmers and other stakeholders such as machinery rings or agricultural contractors.

德国南部农场共享数字农业技术——在农场间分析农场和社会人口特征
到目前为止,农业数字化几乎只在大型农场的背景下讨论。然而,正在进行的数字化迟早会影响整个农业部门。事实上,即使是较小的农场也可以从这个机会中受益,并通过采用农场间的组织形式(如农民之间的合作或承包商服务)来盈利地利用数字农业技术。本文旨在更好地理解数字化转型过程,并通过分析农场和社会人口特征来验证相关预测,这些特征可能会影响农场间使用数字农业技术的可能性,无论组织形式如何。方法选择单变量分析方法和双变量分析方法来描述样本。二元回归分析用于分析德国南部农民的书面在线调查结果。假设H1至H10中列出的特征作为二元逻辑回归模型中统计分析的基于理论的概念框架。结果本研究的结果基于对165名农民的调查样本,其中36.4% (n=60)的农民在农场间使用数字农业技术。样本涵盖巴登-符腾堡州的n=89个农场和巴伐利亚州的n=76个农场。大多数农民(87.3%)认为,在向他们解释一次数字技术后,他们完全有能力自信地使用数字技术(x′s= 2.52, s=1.02,量表:1=完全正确,6=根本不正确),其中38.2%的农民在整个农场使用数字农业技术,这意味着他们一起使用数字农业技术。利用二元逻辑回归模型分析相关的业务和社会人口特征,确定了可能影响小规模地区农场间使用数字农业技术可能性的某些因素。使用这种方法方法,确定了八个预测因素,其中三个对农场间使用数字农业技术的可能性有积极影响:两名外部劳动力的可用性,农场对“整理”或“其他”活动的关注,如牵马或给牲畜增肥。农场面积小于200公顷,没有明确的接班计划,或者农场管理者年龄在30岁以下的农场不太可能使用农场间数字农业技术。在这项研究中,确定了几个影响因素,这些因素可以在数字农业技术的共享使用中发挥作用,特别是在德国南部小规模地区的农民之间。二元逻辑回归的实证结果显示,数字农业技术对农户间使用数字农业技术的可能性既有正向影响,也有负向影响。农民之间的合作形式在德国南部农场建立和使用资本密集型数字农业系统方面发挥着核心作用。因此,该研究强调,在小规模地区广泛和经济地使用数字农业技术可以很快实现,特别是通过农民与其他利益相关者(如机械环或农业承包商)之间建立的合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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