I. A decision support system to improve planning and management in large irrigation schemes

This paper describes a decision support system (DSS) that was developed to improve planning and management for the large irrigation schemes in the Alentejo region of Portugal. The system was designed to help in the analysis and evaluation of the crops and crop systems that can potentially be cultivated, together with identification of limitations affecting crop selection and crop yields. It integrates socio-economic and biophysical data at the field level to analyse the performance of an irrigation scheme in terms of the adoption of irrigation by farmers and farmers’ incomes. The final output is given in the form of specific actions and policies for the irrigated areas. The DSS was designed initially to be used in the Alqueva project, a large irrigation scheme that is under construction in Alentejo. Nevertheless, the final framework is generic in nature, being suitable for planning and policy evaluation in other large irrigation schemes.     

Changing systems for supporting farmers' decisions: problems,paradigms, and prospects

All correct reasoning is a grand system of tautologies, but only God can make direct use of that fact. The rest of us must painstakingly and fallibly tease out the consequences of our assumptions. (Herbert Simon in ‘The Sciences of the Artificial’, p.15)

Decision support systems (DSS), like other information systems (IS) before them, were designed to serve functions deemed by ‘management scientists’ to be potentially useful to managers. But the unwelcome fact is that the use of agricultural DSSs by managers of farms has been low. This paper probes possible reasons for this through interpretation of agricultural DSS case histories and several strands of relevant social theory. From nine cases of DSS development effort and 14 products interpreted comparatively, a number of generalisations are made that serve as reference points in the following search for explanation in theory.First, the nature of management practice of family farms is explored and differences between the internal structure governing personal action and the scientific approach to practice are contrasted. Next, the interaction between the nature of the particular action/practice and the nature of the DSS is explored. A DSS designed to provide integrated, optimal recommendations for management typifies the DSS as a proxy for a manager's decision process. Examples of elaborate expert systems that simply were not used dramatically illustrate the resistance of family farmers to have their decision processes by-passed. On the other hand, the DSS designed to serve as a tool in a modified decision process is shown to have experienced higher use, by deriving and exploiting ‘deep,’ abstract information about the system, by introducing a powerful ‘logic,’ or a combination of both.A number of the referenced case stories demonstrate the resurgence of the decision support mode whereby the simulator is in the hands of an expert intermediary as an alternative to easy-to-use software in the hands of a farmer. This is the mode of operational research/management science, which preceded the DSS.In comparison with hierarchical organizations, available options for overcoming the persistent ‘problem of implementation’ of the DSS in family farms are inherently weak. This focuses attention on the importance of the relationship between the DSS developer and the potential user. Drawing on a classic typology of possible configurations of ‘understanding’ between the scientist and the manager, four approaches to intervention are discussed. Three entail a degree of engagement that qualifies them as ‘participative.’ But one of these constitutes a departure from the DSS and broader IS traditions that places it in another paradigm. In this ‘mutual understanding’ relationship, intervention intent shifts from educating and persuading to recognition of and respect for other ways of viewing the world. This opens up the opportunities for co-creating information systems that utilise the comparative advantages of both practical and scientific knowledge. Intervention emphasis shifts from prescribing action to facilitating learning in actions.Although the DSS has fallen far short of expectations in its influence on farm management, the experience has been instructive in multiple ways to both farmers and professionals in agriculture. In many cases, farmers learned from the DSS and could then jettison it without loss. From disappointments scientists have sometimes learned what was needed to achieve a better outcome. From collated DSS experiences, important lessons for the future can be drawn.The paper concludes by conjecturing that the future of the DSS and related ISs, while more limited than once imagined, holds promise in four directions: a ‘small’ tool for aiding farmers' tactical decisions; a versatile simulator as a consultant's tool; a versatile simulator as the core of a facilitated ‘learning laboratory,’ and a formal framework that supports regulatory objectives in constraining and documenting farming practice.     

Participatory decision support for agricultural management. A case study from Sri Lanka

Agricultural policy makers were helped to construct and use a decision support system (DSS) to identify problems and assess potential solutions for a river basin in Sri Lanka. Through building the DSS themselves, policy makers should reach better decisions. The main aim of the study was to test whether this could be done using a tool called a Bayesian network (BN) which is accessible to non-specialists and able to provide a generic, flexible framework for the construction of DSS. Results from a workshop indicated that the approach showed promise, providing a common framework for discussion and allowing policy makers to structure complex systems from a multi-disciplinary perspective. The need for a multi-disciplinary perspective was clearly demonstrated. The study also suggested improvements to the ways in which BNs can be used in practice. Further workshops with farmers highlighted the importance of involving them in the planning process and suggested more effective ways of doing this while using BNs.     

The influence of agronomic advice upon soil water thresholds used for planting decisions in Southern Queensland’s grains region

At least two decades of sustained research, development and extension (RD&E) effort was undertaken in Southern Queensland’s broad acre cropping zone regarding the role of stored soil water in crop performance. Grain industry concern that the resulting insights into stored soil water were not being integrated into the planting decisions of grain producers was raised when a series of dry years culminated in widespread wheat crop failure across Southern Queensland’s Darling Downs in the winter of 2007. This paper reports on a resulting qualitative investigation into the use of stored soil water research in planting decisions in this cropping region of Australia. A dual sample of grain producer and agronomic RD&E advisors were interviewed in-depth in order to establish what planting strategies were used by grain producers, explore the relationship between these strategies and agronomic advice, as well as the relationship between grain grower’s planting decisions and their short and long term economic objectives. We found that all of the interviewees understood the role of stored soil water in crop performance. However, this understanding supported three distinct planting decision strategies: plant only when a stored soil water threshold has been reached; take the opportunity to plant at least some crop each season; and plant at the appropriate time to maximise crop yield and consider stored soil water a bonus. These planting strategies were perceived by the interviewees to be aligned to agronomic advice differentiated by its commercial terms. Private agronomists, hired via an annual retainer, tended to be associated with the first planting strategy while retail agronomists, hired through the purchase of chemicals, were perceived as associated with the second strategy. These results indicate that an industry wide comparison of planting strategies in terms of yield outcomes and economic performance over multiple years is warranted in order to facilitate industry wide discussion of the trade-offs between long term enterprise profitability and short term economic pressures.     

A conceptual framework for guiding the participatory development of agricultural decision support systems

Scientists develop decision support systems (DSSs) to make agricultural science more accessible for farmers and extension officers. Despite the growing use of participatory approaches in agricultural DSS development, reflection on this endeavour has largely focused on the ‘doing’ of participation or the ‘problem of implementation’ when DSSs have not been adopted by stakeholders. There has been little reference to relevant theoretical approaches to the social processes involved in ‘participation’ or ‘implementation’. However, if DSS use is to reach its full potential, a more conceptually informed understanding of how stakeholders collaborate in the participatory development of DSSs is required. To contribute to this conceptualisation, we developed a framework based on three concepts drawn from the field of science and technology studies: technological frames, interpretative flexibility and boundary objects. The framework highlights the importance and value of social learning for participatory DSS development, which relies upon exploring the participating parties’ different perspectives on the agricultural system represented in the DSS. Our framework provides a broad definition of success for participatory DSS development, placing greater weight on learning during the participatory process compared with subsequent use of the DSS by farmers and/or advisors. Two case studies of stakeholder collaboration to develop an irrigation scheduling DSS for sugarcane production were used to explore the relevance of the framework. The concepts in the framework were clearly displayed during the case studies. At the conclusion of the studies there were contrasting outcomes for the DSS. One group of farmers was keen to apply it in their ongoing irrigation management, while another saw little relative advantage in use of the DSS. In both instances co-learning occurred amongst case study participants, so the participatory process was clearly a success.     

HydroLOGIC: An irrigation management system for Australian cotton

The worldwide need to improve water use efficiency within irrigated agriculture has been recognised in response to environmental concerns and conflicts in resource use. Within the Australian cotton industry, the imperative to reduce water use and optimise irrigation management through the understanding of risk, using information generated by computerised decision aids was identified and subsequently developed into the HydroLOGIC irrigation management software. This paper summarises the attributes of the HydroLOGIC irrigation management software, with particular emphasis on functionality and its application to irrigation decisions within the Australian cotton industry. The software development process is documented to provide direction for future software application initiatives, with particular emphasis on a process of user feedback, evaluation and support requirements providing direction to software development. On-farm experiments throughout the development period allowed the validation of internal software logic, irrigator decision processes, and the OZCOT cotton growth model. The software demonstrated the ability to improve yield and water use efficiency by optimising strategic and tactical irrigation decisions in the Australian furrow irrigation cotton production system. In 7 of the 11 on-farm experiments conducted, the use of HydroLOGIC helped improve overall field water use efficiency by optimising the timing of irrigation events or by indicating further irrigations would not provide yield or maturity benefits. The paper also presents useful insights into the development of software targeted for irrigation utilising in-field measurements of soil water, crop growth and a crop growth simulation model.     

Crop protection in European maize-based cropping systems: Current practices and recommendations for innovative Integrated Pest Management

Maize-based cropping systems (MBCSs), with different frequency of maize in the crop sequence, are common in European arable systems. Pesticide use differs according to the type of active ingredients and target organisms in different regions. Within the EU Network of Excellence ENDURE, two expert-based surveys were conducted focusing on four European study regions where experts were asked to identify MBCSs in their region, determine the current crop protection practices, propose advanced practices against major pests, weeds and diseases, and evaluate the potential agronomic, environmental, economic and social impact of innovative Integrated Pest Management (IPM) tools on the sustainability of MBCSs. In the northern region (Denmark and The Netherlands), maize is mostly cultivated as non-irrigated continuous silage maize or rotated with grass, while in the central-eastern region the major systems are non-irrigated continuous grain maize (Tolna county, Hungary) or grain maize grown in rotation with winter wheat, oilseed rape and sunflower (Békés county, Hungary). In the south-western (Ebro Valley, Spain) and southern (Po Valley, Italy) regions, continuous and irrigated grain maize, as well as irrigated grain and silage maize/winter wheat rotations are prevalent. Differences in current and proposed advanced crop protection practices for MBCSs were identified between regions due to specific pest, weed and disease problems. The tolerant/resistant non-GM maize cultivars, early detection methods, pest and disease forecasting models, precision/patch spraying using GPS spray maps and the community-based decisions through information sharing were commonly recommended for innovative IPM implementation in all regions. Deviations in the recommendations between regions were mainly caused by differences in the evaluation of economic or social impact of some tools (i.e. innovative mechanical weeding). Applied multi-disciplinary research and farmer incentives to encourage the adoption of innovative IPM strategies are essential for sustainable MBCSs development in Europe. The introduction of innovative tools into IPM strategies can contribute significantly to addressing the EU’s strategic commitment to the sustainable use of pesticides and, consequently, more environmentally sustainable MBCSs.     

Econometric analysis of the determinants of adoption of rainwater harvesting and supplementary irrigation technology (RHSIT) in the semiarid Loess Plateau of China

Using a binary logistic regression model, this paper evaluates the determinants of farmers’ decisions to adopt rainwater harvesting and supplementary irrigation technology (RHSIT) and its elasticity of adoption in the rain-fed farming systems, based on a survey of 218 farmers in the semiarid areas of Loess Plateau in 2005. The results indicate that 12 variables are significant in explaining farmers’ adoption decisions. Farmers’ educational background, active labor force size, contact with extension, participation in the Grain-for-Green project, and positive attitudes towards RHSIT are some of the variables that have significantly positive effects on adoption of RHSIT, while farmer's age and distance from water storage tanks to farmers’ dwellings have significantly negative correlation with adoption. The probability of adoption also increases with increased targeting of institutional variables: credit obtained, assistance obtained, and technical training received. Farmers in villages that have more erosion problems are more likely to adopt RHSIT. Besides, the model indicates that a 1 unit increase in the diversity of irrigated crops grown by a household, especially high-value crops, results in a 6.98 times increase in the probability of RHSIT adoption. Variables such as family size, off-farm activity, level of family income, risk preference, and land tenure do not significantly influence adoption. This information will help prioritize the factors that affect adoption decisions and provide insight on pathways to increase the adoption of RHSIT.     

鱼塘机械增氧管理决策支持系统（摘选）

设计合理的决策支持系统（DSS）是一种基于软件的互动系统,旨在帮助决策者根据不同来源定义和解决问题,编译有用的信息。基于农场提供的最大增氧效率的有效数据,利用Microsoft Excel 2007电子表格设计了一款用于鱼塘机械增氧工艺管理的决策支持系统。该系统的使用将有助于农民和推广人员选择高效的增氧机和确定机组数量,并且在保证各鱼塘保持溶解氧含量在安全范围内的情况下,算得各鱼塘增氧机的功率和运转时间。电子表格方式适合于3种温水生长的鱼,分别为罗非鱼、梭鱼和鲤鱼。在设定实地条件下,电子表格计算结果与参考数据进行比较,结果表明电子表格结果符合参考值。在田间以机械（MAP）和非机械（NMAP）增氧数据对电子表格进行验证。利用该系统管理MAP能增产3.3%,与NMAP相比还节省了61.80%的水交换。试验结果表明,对于不同池塘水环境和鱼种类,决策支持系统精度提高的进一步研究中可以应用电子表格。      

Emerging consensus on desirable characteristics of tools to support farmers’ management of climate risk in Australia

The prospect that decision support systems (DSS) can help farmers adjust their management to suit seasonal conditions by putting scientific knowledge and rational risk management algorithms at farmers’ fingertips continues to challenge the science and extension community. A number of reviews of agricultural DSS have called for a re-appraisal of the field and for the need to reflect on past mistakes and to learn from social and management theory. The objective of this paper was to investigate whether there is an emerging consensus, among stakeholders in DSS for Australian agriculture, about the lessons learned from past experience with DSS tools. This investigation was conducted in three parts. The first part was a distillation of suggestions for best practice from the relevant literature. The second part was a reflection on what the champions of five current DSS development and delivery efforts in Australia learned from their recent efforts. The third part tested the level of support for the combined findings from the first and second approaches by surveying 23 stakeholders in the research, development, delivery and funding of DSS.The key propositions relating to best practice that were supported by the survey, listed according to the strength of support, were: 1. It is essential to have a plan for delivery of the DSS beyond the initial funding period. 2. DSS need to be embedded in a support network consisting of farmers, consultants and researchers. 3. DSS development requires the commitment of a critical mass of appropriately skilled people. 4. A DSS should aim to educate farmers’ intuition rather than replace it with optimised recommendations. 5. A DSS should enable users to experiment with options that satisfy their needs rather than attempt to present ‘optimised’ solutions. 6. DSS tools stand on the quality and authority of their underlying science and require ongoing improvement, testing and validation. 7. DSS development should not commence unless it is backed by marketing information and a plan for delivery of the DSS beyond the initial funding period.While the DSS stakeholders supported the proposition that it is essential to have a plan for delivery of a DSS beyond the funding period, the majority resisted the notion of DSS development being market-driven and especially commercial delivery of DSS. We argue that since public funding of the delivery of DSS for farmers’ management of climate risk is highly unlikely, reaping the benefits of lessons learned from past efforts will require that DSS stakeholders change their perception of the commercial delivery model or find an alternative way to fund the delivery of DSS beyond the R&D phase.     

A cognitive systems framework to inform delivery of analytic support for farmers’ intuitive management under seasonal climatic variability

The idea of the decision support system (DSS) for farmers remains an enigma. Clever technology to bridge the gap between agricultural science and farming practice still seems appropriate. Many more of the conditions for success appear to exist today than ever before. Yet the DSS has yet to significantly colonise farm decision making practice. This paper comes late in a long program of research conducted to see if, and under what conditions, computer simulation of farming scenarios, on which a DSS generally depends, can be valued by farmers.The research approach used an unconventional prototypic information system (IS), comprising local measurements, models, and facilitated discussions that evolved in an action research program. The aim has been to elucidate the means by which successful simulation-based decision support intervention can take place and why it usually does not. This required a significant expansion of the researchers’ concept of the farm as a system to include the farmer’s internal system of practical knowing and learning. This paper reports on a cognitive framework model with transactions at interfaces with both the production system and the analytical IS. Its coarse structure is the classical perception-action cycle influenced by goals and outcome feedback. In the highly uncertain production environment of Australian dryland farming, personal judgement plays a significant mediating role between perception and action, and theory of a continuum between the judgement modes of intuition and analysis adds to framework structure. Further structure comes from the theoretical distinctions between holistic and arbitrary intuition, and between causal and probabilistic analysis. Analytic interventions influence: (a) awareness of current situation conditions and (b) expectations of future conditions and action outcomes, and these serve as primary cognitive resources for evaluation of possible actions in planning and decision making. A theory that matches our research experience in bridging the gap between analytic intervention and intuitive practice posits that virtual situations simulated with analytic models and outputs represented graphically can facilitate vicarious experiential learning. This dovetails with theory concerning the education of intuition.The paper concludes by applying criteria from the field of cognitive engineering to test whether the framework presents a concept of mind that is workable for informing practical model-based research and development aimed at supporting farmers’ judgments and decisions.     

稻麦收获机械选型决策支持系统的开发

以农业智能系统PAID4.0为平台,研究开发稻麦收获机械选型的决策支持系统。该决策支持系统由5大部分组成,包括用户界面(人机会话系统)、智能决策模块、数据库及其管理系统、知识库及其管理系统和数据查询模块。将计算机技术和稻麦收获机械选型的专家经验知识相结合,在综合考虑功率和可靠性等因素的前提下给出备选机型,计算单位作业成本,为用户提供决策支持。应用举例表明,该系统操作方便,选型结果可靠性较高。     

Analysing farming practices to develop a numerical,operational model of farmers’ decision-making processes: An irrigated hay cropping system in France

Conventional water management in the Crau plain needs to be modified to ensure higher irrigation efficiency, better crop production and quality, and reduced environmental impacts. Because experimental approaches to test modifications of water management are difficult in relation to border irrigation, simulation may provide an alternative. We describe the development of a conceptual model of the decision-making process that determines the irrigation management of a cropping system, on which a simulation model is to be based. Interviews focused on water management were carried out, to understand how farmers manage their irrigation and how their decisions determine the technical system applied on the farm. These interviews were then analyzed using the “model for action” concept, to generate a conceptual model of the decision system, which is organized as a sequence of decision rules describing irrigation management. This model contains five elements: (1) spatial and temporal factors relevant to decision-making in terms of irrigation and hay cropping; (2) no interaction between the grassland cropping system and the sheep rearing system; (3) five rules to describe irrigation management in the cropping system; (4) major water distribution constraints; and (5) two inter-related operations, hay mowing and irrigation. The rules for irrigation decision-making are written as: “If <Indicator><Operator><Threshold> Then <Action1> Else <Action2>”. This conceptual model was used as the basis of a decision support system that includes models of grass growth and hydrology.     

Ex-ante economic analysis of alternative mulch-based management systems for sustainable plantain production in Southeastern Nigeria

Due to rapid loss of soil fertility in traditional cropping systems and subsequent yield decline, plantain fields in Southeastern Nigeria are usually reverted to fallow after three years. This study investigates the potential of mulch-based systems for long term plantain production: alley cropping with Dactyladenia barteri and natural bush, and a cut-and-carry technique with Pennisetum purpureum. Model results simulate yields decline observed under traditional cropping systems due to a decrease in soil fertility. The adoption of mulch-based technologies is conditioned by capital availability. However, when adequate capital is available, mulch-based systems are quite profitable. Under baseline conditions, the alley cropping system with natural bush outperforms the other two improved technologies with a 154% and 72% increase in net returns over continuous and shifting traditional systems, respectively. The traditional system had the worst performance without fertilizer use. A sensitivity analysis shows that the increase in net return due to fertilizer in the alley cropping and traditional systems is at least 25%. This analysis indicates that credit programs that reach small farmers and land reform that gives them secure title are essential to widespread adoption of mulch-based plantain systems.     

DAIRYPRO—a knowledge-based decision support system for strategic planning on sub-tropical dairy farms. I. System description

DAIRYPRO is a combination decision support and expert system consisting of two modules. The system is designed to help dairy farmers in northern Australia make strategic decisions about their farm. It can be run by dairy extension officers as a consultation package for farmers. The system is based on a combination of statistical models developed from real farm survey data and opinions from experts in the field of dairy farming. The first module gathers together the data needed to run predictive models and the system of rules that enable the program to make estimates of regional average production (using predictive statistical models) and achievable production (using heuristics). These predictions can be compared to the farmer's actual production. Farmers are then encouraged to make hypothetical changes to the inputs on their farm, and `what-if' scenarios of increased or decreased milk production are displayed. The profit or loss associated with these changes is determined. The second module of DAIRYPRO uses the `rules of thumb' of an expert to determine how four pre-defined components of the dairy farm compare to optimum performance. These components are: the winter feeding program, summer feeding program, concentrate feeding program and capital and labour inputs. DAIRYPRO is a useful decision support package for dairy farmers, bank managers, loans officers and farm consultants. ©     

Water user associations in Inner Mongolia: Factors that influence farmers to join

Irrigation districts in Inner Mongolia face problems that are familiar to irrigation areas around the world: shortage of water resources, poor management of water, inefficient use of water resources, and imbalance of financial revenues and expenditures. Water user associations have been promoted to address water supply problems and to encourage efficient water use. In this study, farmers from three distinct areas in Inner Mongolia were surveyed to determine their level of understanding of water user associations and the factors that affect their becoming members. A majority of respondents thought that water user associations were useful to safeguard farmers’ interests, to help reduce labour inputs and disputes about water, to reduce irrigation costs, and to promote efficient water use. The factors found to be most important were: (1) being a village cadre; (2) good state of health; (3) high degree of understanding about water user associations; (4) small percentage of the household in the labour force; (5) cropping income a high percentage of family income; (6) having had previous conflicts involving water use issues.     

Factors underlying the investment decision in energy-saving systems in Dutch horticulture

In the Netherlands the greenhouse sector is a major user of energy. It accounts for 7% of the total national energy use and for 79% of the total energy use in agriculture. In order to sustain this sector on the long term, it is important that its use of energy is lowered. One way of reducing energy use by horticultural producers is investing in energy-saving systems. The purpose of this paper is to provide a better understanding of the investment behavior of firm operators in the adoption of energy-saving systems. Research objectives of the paper are (1) to analyze factors underlying the decision to invest, (2) to explore factors underlying the optimal size of investments. Three investment theories were used to construct an empirical model of investment. Consequently, this model was estimated in a two-stage procedure to analyze the factors influencing the decision of farmers to invest and the level of investments. The paper ends with policy implications.     

Review of irrigation system performance with respect to initial objectives

This paper presents the results of assessment studies of the performance of gravity irrigation projects, in six countries in different climate and social environments, with respect to their original objectives in terms of water availability, water use efficiencies, equity of water distribution, cropping intensity and crop yields, and project economic rates of return.An important lesson is the need for more realistic assumptions in the adoption of design standards, especially irrigation efficiency which affect the cropping intensity, the overall productivity of the project and its economic viability. This comparative method of performance assessment applied to a variety of projects has also provided some useful insights into the relative value of the different approaches to design of gravity irrigation systems.The views expressed in this paper are those of the author and are not presented as official views of the World Bank.     

PCYield: model-based decision support for soybean production

Soybean farmers make decisions based on diverse information. However, one tool not widely utilized is crop simulation. Perceived barriers are burdensome inputs, user unfriendliness, and hard-to-interpret results. Our approach combines the CROPGRO-Soybean model with a window-based, user interface addressing these problems. The resulting, simple-to-use program, PCYield, contains features of more complex systems including (1) field-specific data management, (2) Internet access to real-time weather data, (3) production risk indicators, and (4) graphical outputs. A unique aspect of this project was the participation of a private weather data corporation and, through them, a national chain of agricultural input and service providers. The paper describes the project history, developmental policy issues, technical problems, market testing and subsequent software privatization. Positive producer reactions have included requests for more advanced features. This suggests that grower acceptance of complex models can be gained over time by beginning with simple aids and enhancing them with user-requested upgrades.     

互联网使用对农机服务采纳的影响及其异质性

现代信息技术在农业农村的普及促进了“互联网+农机服务”的融合,是提升农业机械化水平的重要路径,因而探究互联网使用对农机服务采纳的影响具有重要意义。以2014年、2016年和2018年的三期中国家庭追踪调查（CFPS）数据为基础,利用面板Probit模型分析互联网使用是否会影响农户对农机服务的采纳及其异质性。互联网使用对农机服务采纳具有显著的正向影响,农户家庭互联网使用率每提高1个百分点,农机服务采纳概率增加0.068个百分点,运用工具变量法解决内生性问题后,依然得到稳健性结论;互联网使用对男性、中青年、较高教育程度农户农机服务采纳的影响更大,说明个体的学习与劳动能力会影响互联网使用的效果;互联网使用对无家用农机户农机服务采纳的影响系数为0.332远大于对有家用农机户的0.175,说明互联网使用是促进传统小农户农机服务采纳的重要手段;互联网使用对非平原地区农户的农机服务采纳显著为正,边际效应为0.114,比基准模型高出了0.046个百分点,说明互联网信息技术对非平原地区农业机械化发展具有突出贡献。因此,建立“互联网+”的农机服务信息平台,加大对农村地区互联网基础设施的建设,加强对农户的互联网信息技术培训,是支持农机服务业发展和推进农业机械化进程的重要政策手段。