Farmers’ perception of precision technology: The case of autosteer adoption by cotton farmers

Precision agriculture and autosteer technology are, overall, profitable investments for farmers, as previous literature has established. However, what has not been investigated is whether or not farmers perceive these technologies as such. This research postulates that cotton farmers must see potential for higher profits as a result of adopting precision technologies in order to adopt it. Using the 2009 Southern Cotton Precision Farming Survey and multinomial logit model, this research investigates farmers’ perception of precision agriculture and how those perceptions impact adoption of the autosteer GPS guidance system. Autosteer adoption was found to be significant and positively related to the perceived future importance of precision agriculture as well as farmers’ ranking of input cost savings relative to other attributes of the autosteer GPS technology. Additionally, results show that the attributes of the cotton picker are also important factors in adoption of autosteer GPS technology.     

Mapping Potential Crop Management Zones within Fields: Use of Yield-map Series and Patterns of Soil Physical Properties Identified by Electromagnetic Induction Sensing

Investment in precision farming technologies can be expensive and is not expected to be cost-effective for every farm. Previous research and farm experience has shown that the amount of soil variability across a farm and within a field is of key importance for determining potential benefits from the adoption of precision farming. The research reported here evaluates the analysis of yield map sequences and electromagnetic induction (EMI) soil sensing as potentially cost-effective methods for identifying and mapping soil-determined management zones within fields. Both methods are shown to provide useful information for the provisional delineation of soil type boundaries and crop management zones, though soil examination in the field is still necessary to confirm specific soil characteristics.     

The adoption of drones in German agriculture: a structural equation model

Drones are one of the latest tools to have been added to farmers’ precision agriculture technology tool kit. Despite the proclaimed benefits, adoption rates of drones are low and literature regarding the adoption of drones in agriculture is scarce. Therefore, this study investigates whether an extended Technology Acceptance Model (TAM) can contribute to the understanding of latent factors influencing farmers’ intention to adopt a drone. The sample of 167 German farmers was collected in 2019 via an online survey. Using partial least squares structural equation modelling and a binary model, the TAM explains 69% of the variance in the intention to use a drone by German farmers. According to the results, raising farmers’ awareness of farm-specific areas of drone application and the confidence level of using a drone can increase farmers’ intention to adopt a drone. The results are of interest for agribusinesses developing drones as well as selling or providing drones. Furthermore, the results are of interest for researchers in precision agriculture technologies.

     

Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the US

Precision agriculture (PA) technologies have been commercially available since the early 1990s. However, not only has the pace of adoption in the US been relatively modest but a surprisingly large number of producers are not familiar with these technologies. Using farm level survey data, this study quantifies the role that awareness plays in the decision to adopt PA technology and allows us to explore the potential for public or private information programs to affect the diffusion of PA. PA adoption and awareness are modeled as jointly determined dichotomous variables and their determinants are estimated using a two-stage (i.e. instrumental variable) logistic specification. The first-stage logit model indicated that operator education and computer literacy, full-time farming, and farm size positively affected the probability of PA awareness while the effect of age was negative. Grain and oilseed farms (i.e. corn, soybean, and small grains) and specialty crop farms (i.e. fruits, vegetables, and nuts) as well as farms located in the Heartland and Northern Great Plains regions were most likely to be aware of PA technologies. The second-stage PA adoption logit model, which included an instrumental variable to account for the endogeneity of awareness, revealed that farm size, full-time farming, and computer literacy positively influenced the likelihood of PA adoption. Grain and oilseed farms were the most likely types of farms to adopt PA as were farms in the Heartland region. Awareness, as defined in this study, was not found to be limiting the adoption of PA, suggesting that farmers for whom the technology is profitable are already aware of the technology and that a sector-wide public or private initiative to disseminate PA information would not likely have a major impact on PA diffusion.     

Timing of precision agriculture technology adoption in US cotton production

The timing of technology adoption is influenced by profitability and farmer ability to bear risk. Innovators are typically more risk tolerant than laggards. Understanding the factors influencing early adoption of precision agriculture (PA) technologies by cotton farmers is important for anticipating technology diffusion over time. The factors influencing the timing of grid soil sampling (GSS), yield monitoring (YMR) and remote sensing (RMS) adoption by cotton producers was evaluated using multivariate censored regression. Data for cotton farmers in 12 states were obtained from a survey conducted in 2009. The factors hypothesized to influence the timing of adoption included farm characteristics, operator characteristics, PA information sources, adoption of other PA technologies, and farm location. The results suggest that different factors influenced when cotton farmers adopted GSS, YMR and RMS after these technologies became commercially available. For example, land ownership was associated with the timing of GSS adoption, but not YMR or RMS adoption; farmer age was correlated with the timing of GSS and YMR adoption, but not RMS adoption; and obtaining PA information from consultants affected the timing of GSS and RMS adoption, but not YMR adoption. The only factors correlated with the early adoption of all three technologies were beliefs that PA would improve environmental quality and the adoption of at least one other PA technology. Thus, the potential for improved environmental quality appears to be a strong adoption motivator across PA technologies, as is the earlier adoption of other PA technologies. This research may be useful for farmers, researchers, Extension personnel, machinery manufacturers, PA information providers and agricultural retailers to anticipate the future adoption of new and emerging PA technologies.     

Risk preferences and the low-carbon agricultural technology adoption: Evidence from rice production in China

Climate change which is mainly caused by carbon emissions is a global problem affecting the economic development and well-being of human society. Low-carbon agriculture is of particular significance in slowing down global warming and reaching the goal of “carbon peak and carbon neutrality”. Therefore, taking straw incorporation as an example, this paper aims to investigate the impact of risk preferences on farmers’ low-carbon agricultural technology (LCAT) adoption. Based on a two-phase micro-survey data of 1 038 rice farmers in Jiangsu, Jiangxi, and Hunan provinces, this paper uses experimental economics methods to measure farmers’ risk aversion and loss aversion to obtain the real risk preferences information of the farmers. We also explore the data to examine the actual LCAT adoption behavior of farmers. The results revealed that both risk aversion and loss aversion significantly inhibit farmers’ LCAT adoption: more risk-averse or more loss-averse farmers are less likely to adopt LCAT. It is further found that crop insurance, farm scale and governmental regulations can alleviate the negative impact of risk aversion and loss aversion on farmers’ LCAT adoption. Therefore, we propose that local governments need to promote low-carbon agricultural development by propagating the benefits of LCAT, extending crop insurance, promoting appropriate scale operations, and strengthening governmental regulations to promote farmers’ LCAT adoption.     

Does Internet use promote the adoption of agricultural technology? Evidence from 1 449 farm households in 14 Chinese provinces

China is characterized as ‘a large country with many smallholder farmers’ whose participation in modern agriculture is key to the country's modern agriculture development. Promoting smallholder farmers’ adoption of modern agricultural production technology is one effective way to improve the capabilities of smallholder farmers. This paper aims to explore the impact of Internet use on the adoption of agricultural production technology by smallholder farmers based on a survey of 1 449 smallholders across 14 provinces in China. The results suggest that Internet use can significantly promote technology adoption, with the probability of adopting new crop varieties, water-saving irrigation technology and straw-returning technology increasing by 0.200, 0.157 and 0.155, respectively. Furthermore, the effect of Internet use is found to be heterogeneous with a greater effect on smallholder farmers having low education levels, limited training, and high incomes. To increase agricultural production technology adoption by smallholders, rural Internet infrastructure and Internet use promotion should be the focus for the Chinese government.     

Farm adoption of embodied knowledge and information intensive precision agriculture technology bundles

Precision Agriculture - On-farm adoption of individual and groups of precision agriculture technologies has grown in the past 15 years. Based on a sample of 545 farm observations collected by the...     

Perceived importance of precision farming technologies in improving phosphorus and potassium efficiency in cotton production

Site-specific information technologies (IT) provide knowledge about the spatial variability within a field to improve the efficiency of inputs through variable-rate (VR) applications. Identifying factors that influence farmers’ perceptions of the importance of precision farming (PF) technologies in improving the efficiency of phosphorus (P) and potassium (K) fertilizer applications can help to determine why different groups of farmers adopt PF. Knowing these factors can be useful in targeting specific groups of farmers to adopt PF and increase fertilizer efficiency to meet crop needs and reduce P and K losses to the environment. Data were obtained from a 2001 mail survey of cotton (Gossypium hirsutum L.) farmers in six southeastern states in the United States of America. Ordered logit analysis was used to evaluate the level of importance to those who had adopted PF technologies placed on such technologies they had used to improve the efficiency of P and K applications. Results showed that such farmers found soil sampling by management zone or on a grid, and on-the-go sensing most important. Precision farmers who used mapping and remote sensing found PF technologies least important. Older precision farmers who rented larger proportions of their land and used computers for farm management were more likely than other precision farmers to place greater importance on PF technologies in improving the efficiency of P and K applications.     

Farmers’ precision pesticide technology adoption and its influencing factors: Evidence from apple production areas in China

The research aimed to understand farmers’ willingness to adopt (WTA) and willingness to pay (WTP) for precision pesticide technologies and analyzed the determinants of farmers’ decision-making. We used a two-stage approach to consider farmers’ WTA and WTP for precision pesticide technologies. A survey of 545 apple farmers was administered in Bohai Bay and the Loess Plateau in China. The data were analyzed using the double-hurdle model. The results indicated that 78.72% of respondents were willing to apply precision pesticide technologies provided by service organizations such as cooperatives and dedicated enterprises, and 69.72% were willing to buy the equipment for using precision pesticide technologies. The results of the determinant analysis indicated that farmers’ perceived perceptions, farm scale, cooperative membership, access to digital information, and availability of financial services had significant and positive impacts on farmers’ WTA precision pesticide technologies. Cooperative membership, technical training, and adherence to environmental regulations increased farmers’ WTP for precision pesticide technologies. Moreover, nonlinear relationships between age, agricultural experience, and farmers’ WTA and WTP for precision pesticide technology services were found.     

契约农业对家庭农场采纳环境友好型技术的影响

  目的  为测土配方施肥技术的推广提供决策依据。  方法  基于全国1 706个种植业家庭农场调查数据，借助Probit模型和倾向得分匹配(PSM)方法实证检验契约农业对家庭农场采纳测土配方施肥技术的影响。  结果  Probit模型结果表明:家庭农场参与契约农业对其采纳测土配方施肥技术具有显著的正向影响，影响系数为0.621；PSM模型结果表明:与未参与契约农业的家庭农场相比，参与契约农业的家庭农场平均处理效应为0.19，技术培训、农场主从事农业规模经营年限、农场主的从业经历、家庭农场具有规范的日常收支记录以及未来扩张意愿对技术采纳也有影响。  结论  契约农业是影响家庭农场采纳测土配方施肥技术的重要因素，政府应进一步加快建立健全契约农业参与机制，支持家庭农场提升资源禀赋水平。图2表3参18     

Grid soil sampling adoption and abandonment in cotton production

Technology adoption in precision agriculture has received considerable attention, while abandonment has received little. Survey data are now available to evaluate adoption and abandonment decisions. Understanding the factors motivating technology adoption and abandonment has implications for educational efforts directed toward improving the efficiency of production inputs and for research and development to improve the value of precision agriculture technologies. The objective of this research was to identify factors motivating the adoption and abandonment of grid soil sampling in precision cotton production. These decisions were evaluated assuming a random utility model. Data were obtained from a 2005 survey of cotton producers in 11 Southeastern states in the USA. Results from limited dependent variable regressions indicate that younger producers who farmed more cotton area, owned more of their cropland, planted larger amounts of non-cotton area, used a computer for farm management and used a Personal Digital Assistant (PDA) in the field were more likely to adopt grid soil sampling for cotton precision farming. Results also suggest that producers with more cotton area who owned livestock and adopted management zone soil sampling were more likely to abandon grid soil sampling, while those who used a PDA in the field, used grid soil sampling for more years and followed up grid soil sampling with variable-rate fertilizer application were less likely to abandon grid soil sampling for cotton production.     

What would farmers do? Adaptation intentions under a Corn Belt climate change scenario

This paper examines farmer intentions to adapt to global climate change by analyzing responses to a climate change scenario presented in a survey given to large-scale farmers (n = 4778) across the US Corn Belt in 2012. Adaptive strategies are evaluated in the context of decision making and farmers’ intention to increase their use of three production practices promoted across the Corn Belt: no-till farming, cover crops, and tile drainage. This paper also provides a novel conceptual framework that bridges a typology of adaptation with concepts that help predict intentionality in behavior change models. This conceptual framework was developed to facilitate examination of adaptive decision making in the context of agriculture. This research effort examines key factors that influence farmers’ intentions to increase their use of the practices evaluated given a climate change scenario. Twenty-two covariates are examined across three models developed for no-till farming, cover crops, and tile drainage. Findings highlight that farmers who believed they should adjust their practices to protect their farm from the negative impacts of increased weather variability were more likely to indicate that they would increase their use of each of the practices in response to climate change. Additionally, visiting with other farmers to observe their practices was positively associated with farmers’ intentions to increase their use of the adaptive strategies examined. Farmers who were currently using no-till farming, cover crops, and tile drainage were also more likely to plan to increase their use of these practices in response to increased weather variability associated with climate change. However, farmers who reported high levels of confidence in their current practices were less likely to plan on changing their use of these practices in response to climatic changes.     

Socio-economic and environmental changes related to maize richness in Mexico’s central highlands

The occurrence of genetic erosion in local maize varieties in Mexico is intensely debated. Recent publications from Mexico show contradicting results about the loss of local varieties. Genetic erosion is a complex process, and well-documented examples of actual genetic erosion are not common in the literature. We worked in a region in which adoption of improved varieties was negligible, but other factors affecting maize agriculture were at play. The objectives of the study were to describe changes in maize diversity in the last 10 years and to associate them with socio-economic and environmental changes in a region in Mexico’s Central Highlands. We used richness and abundance of local varieties and diversity indices of races as indicators of maize diversity changes over time. We analyzed statistics and based on interviews we evaluated maize diversity changes between 2005 and 2015. We interviewed 113 farmers on two occasions with intervals from 5 to 10 years. According to climate statistics, rain has declined and temperature has increased. We also found a decrease in the lake level during the past 35 years. The total population in the region has doubled since the 1960s. The indigenous population has not changed significantly. Number of people working in agriculture has decreased since the 1960s. Rain fed agriculture decreased 8.1 % from 1990 to 2007. In four villages studied, farmed land area had decreased between 1995 and 2015. This reduction varies between 22 and 39 % depending on the village. Maize planted area decreased from 9675 to 8115 ha from 2003 to 2014. In the same period, avocado plantations grew from 34 to 786 ha. In despite of these changes, we did not find significant changes in average landraces per farmer (2.13 ± 0.28 in 2015) nor per village (4.15 ± 1.26 in 2015). Significant changes in maize races were not found either (1.91 ± 0.26 per farmer, 2.85 ± 0.86 per village in 2015). These results show that maize landrace diversity in the region is resilient but dynamic.     

Economics of autonomous equipment for arable farms

By collecting more data at a higher resolution and by creating the capacity to implement detailed crop management, autonomous crop equipment has the potential to revolutionise precision agriculture (PA), but unless farmers find autonomous equipment profitable it is unlikely to be widely adopted. The objective of this study was to identify the potential economic implications of autonomous crop equipment for arable agriculture using a grain-oilseed farm in the United Kingdom as an example. The study is possible because the Hands Free Hectare (HFH) demonstration project at Harper Adams University has produced grain with autonomous equipment since 2017. That practical experience showed the technical feasibility of autonomous grain production and provides parameters for farm-level linear programming (LP) to estimate farm management opportunities when autonomous equipment is available. The study shows that arable crop production with autonomous equipment is technically and economically feasible, allowing medium size farms to approach minimum per unit production cost levels. The ability to achieve minimum production costs at relatively modest farm size means that the pressure to “get big or get out” will diminish. Costs of production that are internationally competitive will mean reduced need for government subsidies and greater independence for farmers. The ability of autonomous equipment to achieve minimum production costs even on small, irregularly shaped fields will improve environmental performance of crop agriculture by reducing pressure to remove hedges, fell infield trees and enlarge fields.

     

保护性耕作技术推广过程中农民的认知现状及存在的问题

保护性耕作具有保水、保土等良好的生态效益,已成为可持续农业的重要支撑技术。为了解华北平原保护性耕作在生产和推广过程中农户对保护性耕作的认知情况,在河南省保护性耕作推广示范地区进行农户一对一的问卷调查,以期为保护性耕作技术的完善及技术推广提供依据及指导。分析结果显示,在保护性耕作推广地区,农户普遍接受了保护性耕作模式,玉米的免耕技术和秸秆还田技术得到了大面积的应用,83%的农户对保护性耕作持肯定态度,认为可以培肥地力,保持水土。农户接受保护性耕作主要通过技术推广部门的田间指导,占总数的74%,也多希望通过这种方式进行新技术推广。大部分农户认为秸秆过多、病虫害增加和产量不稳等是限制保护性耕作推广和应用的重要因素,可以通过农机农艺结合、加强政府扶持、选育优良品种和改进配套技术等措施促进保护性耕作技术的发展和推广。     

Phenological analysis of unmanned aerial vehicle based time series of barley imagery with high temporal resolution

Emerging strategies and technologies in agriculture, such as precision farming and phenotyping depend on detailed data on all stages of crop development. Unmanned aerial vehicles promise to deliver such time series as they allow very frequent measurements. In this study, we analyse a field trial with two barley cultivars and two contrasting sowing densities in a random plot design over 2 consecutive years using the aerial images of 28 flight campaigns, providing a very high temporal resolution. From empirically corrected RGB images, we calculated the green-red-vegetation-index (GRVI) and evaluated the time-series for its potential to track the seasonal development of the crop. The time series shows a distinct pattern during crop development that reflected the different developmental stages from germination to harvest. The simultaneous comparison to ground based assessment of phenological stages, allowed us to relate features of the airborne time series to actual events in plant growth and development. The measured GRVI values range from ?0.10 (bare soil) to 0.20 (fully developed crop) and show a clear drop at time of ear pushing and ripening. Lower sowing densities were identified by smaller GRVI values during the vegetative growth phase. Additionally, we could show that the time of corn filling was strongly fixed and happened around 62 days after seeding in both years and under both density treatments. This case study provides a proof-of-concept evaluation how RGB data can be utilized to provide quantitative data in crop management and precision agriculture.     

农民家庭小规模土地经营条件下农业现代化发展探讨

现代化环境下的小规模农业和农业现代化具有可融合的一面。从经验角度看,小规模农业对农业现代化影响有限,表现为:小规模农业对农业机械化的影响有限;小规模农业对农业信息化的影响有限;小规模农业对资金密集型的设施农业发展的影响有限;小规模农业对农田水利化发展的影响存在(但已经弱化)。其原因是有几方面的因素弱化了小规模农业的缺陷:农业机械服务的社会化;农业生产逐步呈现出标准化的趋势;农业生产区域化;农民合作社模式;非农就业以工助农。我国农业现代化的出路是发展基于家庭小规模土地的农业现代化,具体措施为:继续完善机械化、水利化设施;发展比较优势农业;农业经营信息化,用最新技术武装农民和农业;继续发展农业经营社会化服务。     

Precision farming uses typology in arable crop-oriented farms in northern France

The adoption of precision farming techniques (PFTs) has been widely studied targeting specific PFT or farming systems along with the potential benefits of these PFTs in terms of yield or input use. However, few studies have examined how PFTs are adopted and used at the farm level. In this study a preliminary investigation was made of on-farm PFT uses in the Oise region (northern France). Three main PFTs were identified in the area: Global Navigation Satellite Systems (GNSS) guidance, section control, and variable rate (VR) application. For each farm, the use of every PFT was defined by the technical characteristics of the equipment, the field operation(s) concerned, the targeted crop(s), the aim of the use, the PFT adoption drivers, and the perceived impacts by the farmers. These different variables were combined into a typology of PFT uses. The results show that most of the farms combined GNSS guidance for all technical operations and section control, whereas VR application was less common. Section control was largely used by farmers for liquid fertilizers and phytochemical spraying. The typology shows three to five types of use for each PFT, which differ in terms of technique adoption drivers, e.g. reducing on-farm work or adaptation to field morphology. According to literature, economic impacts were found to be the most frequent, however farmers seemed unable to quantify them. Social impacts such as reduced work time and fatigue were also frequent and are becoming the main motivation for using PFT on farms studied. Further research is needed to assess the use trajectories of PFT along with the motivations of each PFT use.

     

What determines irrigation efficiency when farmers face extreme weather events? A field survey of the major wheat producing regions in China

Water availability is a major constraint on grain production in China, therefore, improving irrigation efficiency is particularly important when agriculture faces extreme weather events. This paper first calculates irrigation efficiency with a translog stochastic frontier production function and then investigates what happens when extreme weather events occur via a Tobit model. The estimated results reveal several important features of irrigation practices: i) irrigation efficiency is lower when extreme weather events occur; ii) large variations in irrigation efficiency occur across irrigation facilities; iii) the farm plots exhibit an extreme distribution across efficiency levels; and iv) water-saving techniques, technology adoption, and the maintenance of farmers’ economic resilience are major determinants of irrigation efficiency. Based on these results we propose the following recommendations: i) farmers should balance crop yield and water use; undertake relevant training programs and adopt water-saving techniques; ii) local governments and researchers should help farmers to find the optimal level of irrigation water use based on their own circumstances and provide better water-saving techniques and training programs rather than simply encouraging farmers to invest in irrigation facilities in the most extreme weather years; and iii) the income level of farm households should be increased so as to improve their resilience to natural disasters.