sustainability

Article

Research on Innovative Training on Smart GreenhouseTechnologies for Economic and Environmental Sustainability

Angeliki Kavga 1,* , Vasileios Thomopoulos 2, Pantelis Barouchas 1 , Nikolaos Stefanakis 3

and Aglaia Liopa-Tsakalidi 1

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Citation: Kavga, A.; Thomopoulos,

V.; Barouchas, P.; Stefanakis, N.;

Liopa-Tsakalidi, A. Research

onInnovative Training on Smart

Greenhouse Technologies for

Economic and Environmental

Sustainability. Sustainability 2021, 13,

10536. https://doi.org/10.3390/

su131910536

Academic Editor: José María

Cámara-Zapata

Received: 28 April 2021

Accepted: 16 September 2021

Published: 23 September 2021

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1 Department of Agricultural Science, University of Patras, 26504 Patras, Greece; pbar@upatras.gr (P.B.);aliopa@upatras.gr (A.L.-T.)

2 Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece;vthomopoulos@upatras.gr

3 School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens,Greece; nstefa@chemeng.ntua.gr

* Correspondence: akavga@upatras.gr

Abstract: Great advancements in technologies such as big data analytics, robots, remote sensing,the Internet of Things, decision support systems and artificial intelligence have transformed theagricultural sector. In the greenhouse sector, these technologies help farmers increase their profitsand crop yields while minimizing the production costs, produce in a more environmentally friendlyway and mitigate the risks caused by climate change. In greenhouse farming, especially in theMediterranean region, a lack of knowledge and qualified personnel able to uptake new knowledge,the small size of farms, etc., make it difficult to implement new technologies. Although it is necessaryto demonstrate the advantages of innovations related to sustainable agriculture, there is a littleopportunity for specific training on greenhouse production in cutting-edge technologies. To gaininsight into this problem, questionnaires for greenhouse farmers and intermediaries were developedin multiple choice format and filled in by the stakeholders. A statistical analysis was performed, andthe results are presented in graphical form. In most cases, the findings confirmed that producerswho run small farms, in most cases, have a lack of knowledge, especially on how to manage climatecontrol systems or fertigation systems. The majority of farmers were elderly with a low level ofeducation, which makes it difficult to be aware of the training issues, due to distrust and a lackof innovation culture. Therefore, their strategy was usually survival with cost control. However,young graduates have been recently returning to agriculture, and they are open to training activitiesand innovation. The most desirable training offer should be related to sustainable agriculture andprecision agriculture technologies.

Keywords: innovative training; smart agriculture; greenhouse; technology; sustainability; qualityanalysis

1. Introduction

The current human population growth trend, combined with evolving consumptionhabits, rising demand and food waste, is putting an unprecedented strain on agriculturalsystems and natural resources. As a result, one of the biggest problems that humanity willface in the 21st Century is food supply [1–3]. In Greece, greenhouse crops are one of themost dynamic primary production sectors, and at the same time, this is a cultivation systemto which most precision farming technologies and techniques can be directly applied.

Today, Greece has about 61,000 acres of greenhouses, while climate change in dif-ferent parts of the country is the factor that determines their geographical distribution(Figure 1). Almost half (45%) of the greenhouse areas are found in Crete: in Ierapetra,about 15,500 acres (25% of the whole country) and 11,100 acres in the wider area of Messara

Sustainability 2021, 13, 10536. https://doi.org/10.3390/su131910536 https://www.mdpi.com/journal/sustainability

Sustainability 2021, 13, 10536 2 of 22

and the rest of Crete. Respectively, there are 9000 acres in the Peloponnese and 26,000 acresscattered throughout Greece with special reference to Attica and Central Macedonia [4].

Figure 1. Regional distribution of greenhouses in Greece in 2018 [5].

Of the total number of installed greenhouse units in Greece, 30% are modern and ca-pable of producing safe high-quality products while ensuring high yields by using moderntechnologies and automating a large part of their production process. The main reasons forthe increase of automation in greenhouses are the ever-increasing production size (creationof larger units), increased labour costs (which is still 30–35% of the total production costs),the lack of specialised personnel, the unhealthy conditions in which workers often work,the need for specialisation in the production of agricultural products, the need to producesafe and quality products, as well as saving money and time (verticalisation of work).

The slow adaptation to modern ways of practising agriculture is largely due to theunsatisfactory integration of new techniques and technologies in the production process [6].The introduction of systems based on the measurement and management of informationcan contribute to the optimal management of crops [7]. The modern model of agriculturalproduction no longer requires maximisation of production, but maximization of net profit,i.e., rational management of all inputs and outputs of the system [8]. To this end, the conceptof precision farming or smart farming, based on the use of Information and Communication

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Technologies (ICTs) for the precise control of all inputs and accurate output planning in thefarming system, has been developed and has received a significant global response [9].

In the context of the general economic crisis, the Greek agricultural sector has suffereda significant blow in terms of reduced demand for products and limited necessary resources(water, soil and energy) for its development. At the same time, it suffers strongly fromthe consequences of market instability, as well as the effects of climate change, whichexacerbates the frequency and severity of extreme weather events, resulting in reducedcrop yields, lost production and a further decline in agriculture income [10–12].

In particular, the term precision agriculture refers to an area where sensor technologybrings new capabilities that solve old problems. According to a recent report by the Foodand Agriculture Organization of the United Nations (FAO), between 2005 and 2015, naturaldisasters cost USD96 billion in damage to agricultural and livestock production. The FAOargues that combining agriculture with Internet of Things (IoT) technologies is essential,as global food production must increase by 70% by 2050 to feed 9.6 billion people [13].The goals of precision agriculture are to increase production per area while reducing theresources used (man-hours, water, energy, pesticides and fertilisers) [14].

Agriculture remains a place where IoT applications have not yet been widely used,with an estimated global turnover at EUR 12.7 billion for 2019 and EUR 20.9 billion for2024 [15].

Catastrophic weather, disease and pests adversely affect agricultural production andcause huge economic losses annually. Unfortunately, traditional treatments incur additionalcosts for growers and are largely ineffective. However, in precision agriculture, the integra-tion of sensor–controller technologies and IoT applications can lead to a significant increasein efficiency. Combining the collection of primary data via wireless sensor networksand their processing by Decision Support Systems (DSSs) that monitor the microclimateconditions of the crop can reduce the resources consumed, predict the spread of pestsand contribute to disease prevention and optimised production. The result is improvedmanagement, better food quality and lower costs. Investing in technology solutions andprecision agriculture leads to the modernisation of time-consuming and tedious processesand is an extremely attractive option as it can offer better living conditions and reducedworkload, thus attracting new generations of farmers and scientists in rural areas [16,17].

The current technology used in greenhouses for sustainable production are mainlyclosed hydroponics systems (substrates), irrigation control systems (fertigation) and cli-mate control systems (shading screen, natural and forced ventilation, fan and pad systems,heating systems and fog systems). The adoption of smart farming technologies by green-house farmers is low. Their acceptance is scarce due to distrust and a lack of innovationculture [18]. In some cases, they use remote control management systems, but this isquite rare.

The region of research is the area included within the administrative boundaries of theRegional Units of Greece, mostly Crete, the Peloponnese and Attica, where the greenhousesare concentrated. This work aims to reflect the current level of education of farmers andintermediaries in intelligent greenhouse technologies, as well as their educational needsand how training will contribute to the adoption of intelligent agriculture techniques inGreece and other European countries [19,20].

The present paper is organised as follows. Section 2 describes the sampling, collectionand processing of the raw data. Section 3 presents the results of the farmers’ and intermedi-aries’ questionnaires and the statistical analysis. Finally, Section 4 presents the conclusionsof the research.

2. Materials and Methods2.1. Sampling

The University of Patras and GeoTechnical Chamber of Greece (GeoTEE) designedtwo open and closed types of questionnaires, addressed to farmers and intermediaries(agriculturalists, farming schools and associations, cooperatives, VET organisations, cham-

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bers of commerce and local governments and development agencies) for greenhousesthroughout Greece [21,22].

The sampling method was stratified with a division of the farmers’ cooperatives ofGreece per region and then sorted by cluster category. The sample number was determinedper cluster [23] in combination with the breakdown of cooperative units by regional unitfollowing Bayesian statistical methods [24]. The survey was carried out in the physicalpresence of the researcher at the site of the investigation.

2.2. Data Collection and Processing

The research was conducted using two questionnaires and covering all relevant pa-rameters of training for greenhouse farmers. The key stakeholders reached within theirdomain included staff, members, trainers, farmers, innovative initiatives, farming schools,VET organisations, etc., so that they captured the main issues of training in each sector.

For the research, 47 questionnaires from farmers and 43 from intermediaries werecollected. The distribution was from all over Greece, but mainly from areas with a highconcentration of greenhouses (Crete, the Peloponnese and Attiki). The questionnaires weredisseminated using Google Forms and collected using Google Spreadsheets. The data weregathered and grouped per question.

The data collected included information on the farm, farmer and intermediary charac-teristics, institutional, production and market-related factors. Socioeconomic and demo-graphic variables of the respondents included the farm household head’s gender, level ofeducation, marital status, household size, occupation, access to climate information andits source, access to credit and income, among others. The full lists of the parameters aregiven in Tables 1 and 2.

The data were summarized into descriptive statistics to provide users with the frequen-cies of each variable in the dataset. To further help the readers understand for the collecteddata, suitable measures of central tendency were performed. These measures provide anumerical index of the average score in the distribution [25]. In addition to these measures,the dispersion of the data was considered in order to present how uniformly respondentsanswered the questions [26]. These measures provided the variability or the amount ofchange in the data with the most common measure to be the standard deviation, whichdetermines whether a particular data value is close to or far from the mean. The descriptivestatistical analyses were performed with IBM SPSS [27], and the results are presented inTables 3 and 4 [28,29].

The three top concerns for greenhouse farming were: knowledge, technology and typeof crop. Use of energy, fertigation, costs and selling prices were also common concerns.

To summarize the survey’s findings, greenhouse cultivation lacks updated techno-logical knowledge due to time, skills and attitude constraints. There is an awareness ofavailable solutions, but not of their relevancy and usefulness in their cases. Larger farmunits and younger farmers are more prone to uptake innovation, and this process can beaccelerated for all farmers using training and consulting services.

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Table 1. Analysis of the farmers’ questionnaire for training in greenhouse systems.

Variable Type Variable Name Variable Explanation

Farmer’s attribute Type of exploitation (X1) Personal/companyFamily members employed in the greenhouse (X2) Farmers’ family members employed in the greenhouseGender (X3) The gender of the farmerAge (X4) The age of the farmerEducation level (X5) The level of education

Greenhouse’s attribute Exploitation region (X6) The region where the greenhouse is locatedAgricultural acreage (X7) The area of agricultural landMain cultivation (X8) The main cultivation cropSecondary cultivation (X9) The secondary cultivation cropOther cultivation (X10) Other cultivation cropCultivation system (X11) The cultivation system usedGreenhouse construction (X12) The greenhouse construction typeType of greenhouse (X13) The type of greenhouseFrame materials (X14) Frame materialsCover materials (X15) Cover materialCooling system (X16) Cooling systemHeating system (X17) Heating systemEnergy source (X18) Energy sourceIrrigation system (X19) Irrigation systemAutomatic controller (X20) Is an automatic controller used?Pest and disease control system (X21) Pest and disease control system

Crop selection criteria (X22)The criteria by which the farmers decide on the type ofcrop they will grow the next season in their greenhouse

Greenhouse technological developmentclassification (X23)

Classification of the greenhouse in terms oftechnological development

Preferable new technology (X24) What new technology could be applied in the greenhouse?Agricultural consultant (X25) If the greenhouse employs an agricultural consultantWorkers in the greenhouse (X26) Are there any workers employed in the greenhouse?Migrant workers (X27) If the workers are migrantsProduct distribution (X28) How is the product distributed?Product for export (X29) If the product is intended for exportPercentage exported (X30) What percentage of the product is exported?Production certification (X31) If there is a product certificationType of product certification (X32) Which product certification is used?

Socioeconomic factors Source of information about technologicaldevelopments (X33)

How is the farmer informed about the moderntechnological developments of greenhouse crops?

Subsidized programmes (X34)If the greenhouse has been included insubsidized programmes

Which subsidized programmes (X35)

Young Farmers Business (X36)If the farmer has joined the Young FarmersBusiness programme

Agricultural training seminars attended (X33) If the farmer has attended agricultural training seminarsThematic areas interested in for training (X34) Thematic areas farmers are interested in for trainingTraining issues (X35) Training issues faced by farmersSkills to be improved during training (X36) What skills would be improved during training?Rating the different forms of training (X37) Rating the different forms of trainingAppropriate media for training (X38) What is the most appropriate medium for training?Effectiveness of training methods (X39) What are the most effective training methods?Barriers to learning (X40) What are the barriers to learning for the farmers?

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Table 2. Analysis of the intermediaries’ questionnaire for training in greenhouse systems.

Variable Type Variable Name Variable Explanation

Crop systems in the area (X1)Intermediary’s perception concerning the crop systems inthe area

Integrated crop management systems in the area (X2)Intermediary’s perception concerning the integrated cropmanagement systems in the area

Suggested greenhouse system (X3) Which greenhouse system does the intermediary suggest?

Technological systems in greenhouses in the area (X4)What are the technological systems in greenhouses inthe area?

Advanced (cutting-edge) technology systems in greenhousesin the area (X5)

What are the advanced (cutting-edge) technology systems ingreenhouses in the area?

Level of farmers’ training for the use of already installedtechnology (X6)

What is the perceived level of farmers’ training for the use ofalready installed technology?

Greenhouse crop technologies to be immediately adopted(X7)

What are the greenhouse crop technologies that must beadopted immediately?

Services to farmers (X8) What services to the farmers does the intermediary provide?Expected effects of greenhouse crops (X9) What are the expected effects of greenhouse crops?Percentage exported (X10) What percentage of the production is being exported?10-year period cultivation changes (X11) How often do cultivations change in a 10-year period?

Factors with greater impact on the country’s economy (X12)What are the factors with a greater impact on thecountry’s economy?

Crop selection criteria (X13) What are the criteria for the selection of next year’s crop?Level of cutting-edge technology adoption (X14) What is the level of cutting-edge technology adoption?

Obstacles to cutting-edge technology adoption (X15)What are the obstacles to the adoption of cutting-edgetechnology?

Level of interest in training in thematic areas (X16) What is the level of interest in training in thematic areas?Training problems faced by farmers (X17) What are the main training problems faced by farmers?

Degree of advice from consultants (X18)How much do the farmers listen to the intermediary’sadvice?

Importance of farmers greenhouse training (X19) What are the most important fields of training for farmers?

Areas of training that farmers should invest in (X20)What are the areas of training in which the farmers shouldinvest?

Level of farmers interest in training programmes (X21) What is the level of farmers interest in training programmes?Skills farmers will improve in training (X22) What are the skills farmers will improve in training?

Rating different forms of training for farmers (X23a)What is the rating of the different forms of training for thefarmers?

Rating different forms of training for intermediaries (X23b)What is the rating of the different forms of training for theintermediaries?

Training method effectiveness (X24) How effective is each training method?Barriers to learning (X25) What are the main barriers to learning?Factors that could increase the competitiveness of greenhouseproducts (X26)

What factors could increase the competitiveness ofgreenhouse products?

Obstacles to the increase of greenhouse production (X27)What are the obstacles to the increase of greenhouseproduction?

Future of greenhouse technological development rating (X28)How is the future of the greenhouse technologicaldevelopment rating perceived?

Conditions that the farmers will adapt to for newmodern/alternative greenhouse crops (X29)

Under what conditions will the farmers adapt to newmodern/alternative greenhouse crops?

Need for collaboration in regional groups/clusters (X30) Is regional grouping/clustering necessary?

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Table 3. Results of descriptive statistics in the farmers’ questionnaire for training in greenhouse systems.

Greenhouses’ Technological Development Mean Standard Error Standard Deviation Sample Variance Kurtosis Skewness

Greenhouses’ technological development 2.4359 0.1150 0.7180 0.5155 −0.1219 0.0114Level of interest in training in thematic areas Mean Standard error Standard deviation Sample variance Kurtosis Skewness

Climate management/technologies/equipment 2.9574 0.1215 0.8329 0.6938 0.1357 −0.6256Energy and resources management 2.4894 0.1486 1.0188 1.0379 −1.0745 −0.0988

Automation/greenhouse digitalization 3.2128 0.1214 0.8324 0.6929 −0.5468 −0.6613Coverage and PV 2.8298 0.1500 1.0283 1.0574 −0.8015 −0.5207Circular economy 2.1915 0.1510 1.0350 1.0712 −1.0570 0.3351

Irrigation–fertigation–biostimulant indigenous AMFs–nutrient analysis 3.3191 0.1101 0.7549 0.5698 0.5311 −0.9303Business basics, owning/managing a fresh produce business, quality and safety 3.0426 0.1392 0.9546 0.9112 −0.0073 −0.8707

Information and networking for farmers 2.5745 0.1631 1.1179 1.2498 −1.2953 −0.2436National and international produce trade 2.6170 0.1538 1.0540 1.1110 −1.0685 −0.3227

E-skills 2.6809 0.1493 1.0238 1.0481 −1.1582 −0.3227Forms of training rating Mean Standard error Standard deviation Sample variance Kurtosis Skewness

Traditional face-to-face learning 3.1915 0.1447 0.9921 0.9842 −0.2239 −0.9612Virtual and blended learning 2.9024 0.1554 0.9951 0.9902 −1.1744 −0.2766Massive open online courses 2.5610 0.1564 1.0012 1.0024 −1.0387 0.0597

Peer-to-peer learning 2.3571 0.1698 1.1004 1.2108 −1.2866 0.1518Experienced farmers as mentors 2.6170 0.1476 1.0120 1.0241 −1.0004 −0.2024Knowledge sharing mechanisms 2.9302 0.1539 1.0094 1.0188 −0.9850 −0.4378

Apps for learning via a smartphone 2.4773 0.1705 1.1307 1.2785 −1.3745 0.0590Appropriate media for learning Mean Standard error Standard deviation Sample variance Kurtosis Skewness

Presentations 3.1277 0.1312 0.8997 0.8094 −0.6073 −0.6347Videos 2.7619 0.1438 0.9321 0.8688 −0.9934 −0.0630Audio 2.4146 0.1674 1.0718 1.1488 −1.1636 0.2331Text 2.2683 0.1563 1.0006 1.0012 −1.1706 0.0518

Books 2.5106 0.1517 1.0399 1.0814 −1.1338 0.0310Live teacher/seminars 2.8182 0.1568 1.0404 1.0825 −0.9990 −0.3969

Other 2.4889 0.1640 1.1000 1.2101 −1.2951 0.0831Efficiency of training methods Mean Standard error Standard deviation Sample variance Kurtosis Skewness

Classroom sessions/education at the individual level/individual contact 3.1915 0.1348 0.9240 0.8538 −0.0557 −0.9178Short-term seminars/lectures at physical meetings 3.0444 0.1229 0.8245 0.6780 −0.7884 −0.3391

Practical courses/exercises 3.1304 0.1376 0.9335 0.8715 −0.3254 −0.7838Online courses (e-learning) 2.6429 0.1356 0.8785 0.7718 −0.4367 −0.3469

Agriculturalist’s farm visits/educational excursions/visits/field demonstrations 3.2045 0.1439 0.9543 0.9107 −0.1767 −0.9365Online communication with agriculturalist (real time) 2.5455 0.1541 1.0220 1.0444 −1.0720 −0.0600

Farmers agriculturalist’s office visits 2.6087 0.1538 1.0430 1.0879 −1.1317 −0.1182Creating newsgroups/Information in the form of forms/brochures 2.4444 0.1369 0.9184 0.8434 −0.6882 0.2631Television broadcasts/broadcasts on radio/articles in newspapers 2.1591 0.1415 0.9387 0.8811 −0.9847 0.1986

Agricultural journals 2.6667 0.1348 0.9045 0.8182 −0.6217 −0.2357Helpline instructions 2.0465 0.1629 1.0680 1.1406 −0.8295 0.6418

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Table 4. Results of descriptive statistics in the intermediaries’ questionnaire for training in greenhouse systems.

Level of Farmers’ Technology Training Mean Standard Error Standard Deviation Sample Variance Kurtosis Skewness

Level of farmers’ technology training 2.3023 0.1224 0.8028 0.6445 −0.2225 0.2539Cutting-edge technologies’ adoption Mean Standard error Standard deviation Sample variance Kurtosis SkewnessCutting-edge technologies’ adoption 2.1395 0.0975 0.6392 0.4086 −0.4789 −0.1244Expected effects of greenhouse cultivations Mean Standard error Standard deviation Sample variance Kurtosis SkewnessModernization of greenhouses 1.3023 0.0783 0.5134 0.2636 1.1906 1.4342Innovation in the production and distribution of greenhouse products 1.2791 0.0692 0.4539 0.2060 −1.00645 1.0211More efficient use of natural resources 1.3023 0.0783 0.5134 0.2636 1.1906 1.4342Promoting the circular economy for sustainable development and environ-mental protection

1.5116 0.0963 0.6314 0.3987 −0.2410 0.8473

Contribution to the production of competitive products in domestic andinternational markets

1.2558 0.0751 0.4925 0.2425 2.4503 1.7694

Increasing agricultural income and improving the quality of life of farmers 1.2326 0.0732 0.4799 0.2303 3.3132 1.9673Level of impact of greenhouse cultivations on the country’s economy Mean Standard error Standard deviation Sample variance Kurtosis SkewnessThe percentage of modern greenhouses was higher 1.1860 0.0600 0.3937 0.1550 0.8338 1.6725Modernization of greenhouses and the use of state-of-the-art technologiesacted as incentives for young entrepreneurs

1.1395 0.0535 0.3506 0.1229 2.7779 2.1566

National and regional policies included the interconnection of greenhouseswith technological and digital infrastructure

1.4651 0.0902 0.5916 0.3499 −0.1809 0.8645

Organizing into producer groups and certification, combined with offeringhigh-quality products are the pillars of greenhouse crops

1.1860 0.0687 0.4502 0.2027 5.8045 2.4558

The procedures at all stages from the development of the greenhouse to thesale of the produced product were simpler

1.5116 0.0904 0.5925 0.3510 −0.4598 0.6736

Greater emphasis is placed on cooperation between production and researchinstitutes in innovative actions that promote intelligent agriculture

1.3488 0.0807 0.5293 0.2803 0.3422 1.1551

Farmers participate in cooperative schemes such as producergroups/agricultural cooperatives

1.2791 0.0692 0.4539 0.2060 −1.0065 1.0211

Farmers work with clusters 1.3256 0.0796 0.5219 0.2724 0.7261 1.2891Farmers had a better level of knowledge and information 1.0698 0.0393 0.2578 0.0664 10.7552 3.5010Crop selection criteria Mean Standard error Standard deviation Sample variance Kurtosis SkewnessThe cultivation proposed by the public bodies 1.1628 0.2155 1.4131 1.9967 −1.7995 0.4413Cultivation suggested by the agricultural consultant 2.0465 0.1594 1.0455 1.0930 −0.0531 −1.0142The producer decides when the crop will be placed (species and variety),the period of production, etc.

1.5581 0.1384 0.9077 0.8239 −0.72429 0.0194

Cultivation that corresponds to a commercial agreement it has concluded 1.2326 0.0804 0.5272 0.2780 4.4880 2.2687Market demand due to conditions 1.2791 0.0837 0.5488 0.3012 2.7987 1.8841The usual crops of the area 1.4186 0.0761 0.4992 0.2492 −1.9773 0.3420A new crop that they saw applied by another producer/Internet 2.0465 0.1410 0.9246 0.8549 −0.3916 −0.6638Financial incentives for alternative crops 1.7442 0.1492 0.9782 0.9568 −0.9052 −0.2519

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Table 4. Cont.

Level of Farmers’ Technology Training Mean Standard Error Standard Deviation Sample Variance Kurtosis Skewness

Cutting-edge technologies’ adoption obstacles Mean Standard error Standard deviation Sample variance Kurtosis SkewnessDiscontinuities in strategic planning issues 2.8605 0.1271 0.8333 0.6944 1.4457 −1.2807Technical and programming difficulties 2.5581 0.1462 0.9587 0.9192 −0.6614 −0.8534Inability to borrow from financial institutions 2.9535 0.1881 1.2335 1.5216 −1.0417 −0.7856Failure to join a national or European funding program 2.5349 0.1833 1.2021 1.4452 −1.5148 −0.3015Inadequate training in innovation 3.1163 0.1381 0.9053 0.8195 1.2496 −1.2479Lack of financial incentives 2.8837 0.1637 1.0737 1.1528 −0.4674 −0.8482Inadequate access to research at higher education institutions 2.3721 0.1969 1.2914 1.6678 −1.7789 0.0185Lack of synergy with trained scientific staff 2.4651 0.1833 1.2022 1.4452 −1.5809 −0.2159Lack of technical–scientific teams with broad training 2.0930 0.1877 1.2308 1.5150 −1.6207 0.3778Decisions with the sole aim of surviving without a long-term plan 3.2558 0.1454 0.9535 0.9092 1.2919 −1.4121Lack of trust in cutting-edge technology systems 2.3256 0.1990 1.3042 1.7080 −1.7962 0.0983Interest in thematic areas Mean Standard error Standard deviation Sample variance Kurtosis SkewnessClimate management/technologies/equipment 2.1163 0.1209 0.7931 0.6290 0.5546 0.6866Energy and resources management 2.7674 0.0989 0.6487 0.4208 −0.6194 0.2627Automation/greenhouse digitalization 2.7209 0.1385 0.9083 0.8250 −0.4791 −0.4017Coverage and PV 2.0698 0.1306 0.8562 0.7331 −1.1114 0.1011Circular economy 3.1628 0.1242 0.8145 0.6633 1.5600 −1.1459Irrigation–fertigation–biostimulant indigenous AMFs–nutrient analysis 2.7674 0.1044 0.6844 0.4684 −0.7980 0.3324Business basics, owning/managing a fresh produce business, quality andsafety

2.6279 0.1154 0.7567 0.5725 −0.0557 −0.2849

Information and networking for farmers 2.6512 0.1446 0.9483 0.8992 −0.7361 −0.2826National and international produce trade 2.5116 0.1305 0.8556 0.7320 −0.5250 −0.0378E-skills 2.5581 0.1214 0.7959 0.6334 −0.3730 0.0979Training problems greenhouse farmers are facing Mean Standard error Standard deviation Sample variance Kurtosis SkewnessIncomplete offer of specialized programs 2.8837 0.1163 0.7625 0.5814 −0.4730 −0.1360Limited available time 2.7674 0.1096 0.7184 0.5161 −0.2845 −0.0227Scepticism about usefulness in cultivation 2.7674 0.1238 0.8117 0.6589 −0.5132 −0.1004High cost 2.7907 0.1313 0.8607 0.7409 −0.4833 −0.2768Lack of pilot diffusion programs 3.000 0.0941 0.6172 0.3810 −0.1985 0.0000Lack of sufficient knowledge to take further training including e-skills 2.8140 0.1165 0.7639 0.5836 −0.5795 −0.0024Difficulties to manage learning/training software 2.9767 0.1267 0.8306 0.6899 −0.8923 −0.2169Lack of a culture of change 3.1395 0.1226 0.8042 0.6467 −1.3963 −0.2635Lack of interest in participation 2.8837 0.1254 0.8226 0.6766 −0.3817 −0.3149Greenhouse structure limitations 2.3256 0.1090 0.7145 0.5105 0.5053 0.6553Rating of training forms Mean Standard error Standard deviation Sample variance Kurtosis SkewnessPresentations 3.1395 0.1226 0.8042 0.6467 −0.3884 −0.5515Videos 3.2093 0.1225 0.8035 0.6456 0.9172 −0.9830Audio 2.6977 0.1580 1.0359 1.0731 −1.1387 −0.1558Text 1.9535 0.1243 0.8151 0.6645 −0.6693 0.3641Books 1.9302 0.1388 0.9101 0.8283 0.3712 0.9372Live teacher/seminars 3.3488 0.10979 0.7199 0.5183 −0.7856 −0.6428

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Table 4. Cont.

Level of Farmers’ Technology Training Mean Standard Error Standard Deviation Sample Variance Kurtosis Skewness

Training method efficiency Mean Standard error Standard deviation Sample variance Kurtosis SkewnessClassroom sessions/education at the individual level/individual contact 3.3256 0.1233 0.8083 0.6534 0.1502 −0.9592Short-term seminars/lectures at physical meetings 3.1860 0.1211 0.7945 0.6312 −0.1817 −0.6510Practical courses/exercises 3.5116 0.1171 0.7676 0.5891 1.75545 −1.5317Online courses (e-learning) 2.7442 0.1204 0.7896 0.6235 −0.7933 0.1936Agriculturalist’s farms visits/educational excursions 3.5116 0.1122 0.7359 0.5415 −0.0855 −1.1698Online communication with agriculturalist (real time) 2.8605 0.1314 0.8614 0.7420 −1.1816 0.0454Creating newsgroups/information in the form of forms/brochures 2.4651 0.1425 0.9348 0.8738 −0.7687 0.1977Television broadcasts/broadcasts on radio/articles in newspapers 2.6047 0.1417 0.9294 0.8638 −0.7102 −0.2275Agricultural journals 2.4884 0.1262 0.8273 0.6844 −0.4210 0.1710Helpline instructions 2.2093 0.1354 0.8880 0.7885 −0.3849 0.4213

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3. Results and Discussion

The research conducted a descriptive analysis for innovative training on greenhousesin Greece. This was conducted using two questionnaires and covering all relevant parame-ters of training for greenhouse farmers so that they captured the main issues of trainingin this sector. Training offered should be related to sustainable agriculture (reductionof consumption and pollutants and increased resilience of exploitation) and precisionagriculture [30].

The main problem is that many of the farmers were old and had a low level ofeducation, which made it more difficult to be aware of training opportunities, and theypresented scarce acceptance of such issues due to distrust and a lack of innovation culture.The farmers had high uncertainty regarding the training process due to insufficient reliableinformation and little data on the market assuming great production risk. Therefore, theirstrategy was usually survival with important cost control. Recently, young graduates havebeen returning to agriculture, and they are open to training activities and innovation.

All the figures that follow are based on the the raw data processing.

3.1. Farmers

The main farmer age groups were 41–54 and below 40 (Figure 2) [31]. In addition,the farmers mainly had a secondary and tertiary level of education (Figure 3). It is worthmentioning that younger farmers were more educated than older farmers, and they weremainly agriculturalists [32].

The small and medium-to-small size of farmlands in Greece was an important finding(Figure 4). The main cultivation in Greek greenhouses is vegetables with minor cultivationsbeing ornamentals (flower crops) and strawberries (Figure 5). In addition, the maincriterion by which farmers decided on the type of crop they would grow the next season inthe greenhouse corresponded to a commercial agreement that had been concluded or theneeds of the market. A secondary criterion was the judgement of the farmer or the usualcultivation in the wider area (Figure 6).

Figure 2. Farmer age groups.

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Figure 3. Farmer level of education.

Figure 4. Farmer exploitable area in ha.

Figure 5. Main cultivation.

Multispan greenhouses are the most common type in Greece (Figure 7). Most farmersclassified their greenhouses into average categories in terms of technological development(Figure 8). The distribution of the product is mainly done directly or through a group ofproducers or a cooperative (Figure 9).

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Figure 6. Crop selection criteria.

Figure 7. Type of greenhouse

Figure 8. Greenhouses’ technological development.

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Figure 9. Distribution of the product.

Farmers were mostly interested in being trained in irrigation–fertigation–biostimulantindigenous Arbuscular mycorrhizal fungi (AMFs)–nutrient analysis and automation/greenhouse digitalization (Figure 10). Traditional face-to-face learning was the preferredform of training, followed by knowledge sharing mechanisms and virtual/blended learning(Figure 11).

Figure 10. Level of interest in training in thematic areas.

Figure 11. Forms of training rating.

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According to Figure 12, the farmers perceived that the most appropriate media fora learning platform were live teachers/seminars and presentations. Farmers believedthat traditional face-to-face learning and knowledge sharing mechanisms were the mostefficient training methods (Figure 13).

Figure 12. Appropriate media for learning.

Figure 13. Efficiency of training methods.

3.2. Intermediaries

The intermediaries believed that farmers were moderately trained to use the tech-nology already installed in their greenhouses (Figure 14). In addition, the intermediariesbelieved that cutting-edge technologies had been adopted moderately by the farmers(Figure 15).

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Figure 14. Level of farmers’ technology training.

Figure 15. Cutting-edge technologies’ adoption.

Intermediaries believed that irrigation/fertigation systems and greenhouse microcli-mate recording and control systems were the greenhouse crop technologies that should beimmediately adopted by farmers to improve their income (Figure 16). It was apparent thatintegrated crop management systems were the preferred greenhouse system suggested tothe producers (Figure 17) [33].

Figure 16. Technologies for immediate adoption.

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Figure 17. Suggested greenhouse systems.

The intermediaries estimated that the contribution to the production of competitiveproducts in domestic and international markets, increasing agricultural income and im-proving the quality of life of farmers were the most expected effects of greenhouse crops(Figure 18). In addition, they indicated that greenhouse crops would have a greater impacton the country’s economy if farmers had a better level of knowledge, and informationand modernization of greenhouses and the use of state-of-the-art technologies acted asincentives for young entrepreneurs (Figure 19).

Figure 18. Expected effects of greenhouse cultivations.

Figure 19. Level of impact of greenhouse cultivations on the country’s economy.

The intermediaries suggested that the main criteria by which farmers decided on the typeof crop they would grow in their greenhouse were crops that corresponded to a commercial

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agreement that had been concluded and the market demand due to environmental conditions(Figure 20). They also stated that decisions with the sole aim of surviving without a long-termplan and inability to borrow from financial institutions were the main obstacles to the adoptionof cutting-edge technologies by greenhouse farmers (Figure 21).

Figure 20. Crop selection criteria.

Figure 21. Cutting-edge technologies’ adoption obstacles.

According to the intermediaries, irrigation–fertigation–biostimulant indigenous AMFs–nutrient analysis was the most interesting thematic area for farmers to be trained (Figure 22).Furthermore, they supported that the lack of a culture of change and difficulties to managelearning/training software were the basic training problems faced by greenhouse farmers(Figure 23).

Figure 22. Interest in thematic areas.

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Figure 23. Training problems greenhouse farmers are facing.

In conclusion, the intermediaries believed that live teachers/seminars, videos andpresentations were the most appropriate forms of training for farmers (Figure 24). Inter-mediaries also perceived that practical courses/exercises and agriculturalist’s visits tofarms/educational excursions were the most effective training methods (Figure 25).

Figure 24. Rating of training forms.

Figure 25. Training method efficiency.

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4. Conclusions

Farmers’ digital and environmental skills are very important for successful greenhousefarming. Producers who run small farms, in most cases, lack knowledge, especially onhow to manage climate control systems or fertigation systems. Along the value chain,first, the market (production schedule, demand and price), followed by available resources(water, energy and labour, mainly) and, finally, the management of the production processcan affect greenhouse farming. The main issue in innovation uptake by greenhouse farmerswas at first the high uncertainty due to insufficient reliable information. On the one hand,producers had little data on the market and assume the risk of producing without thenecessary guarantees. Therefore, their strategy was usually survival with significant costcontrol. On the other hand, the technical training of farmers on control and the automationof processes in the greenhouse (precision agriculture) was very basic. Finally, the lack ofqualified personnel able to adopt new knowledge was a key factor.

The main issue in the technology adaptability of greenhouse farmers was the lack oftechnical training and distrust. In general, greenhouse farms did not employ enough quali-fied personnel as most of the farms were small, and finding the free time for training actionswas a big challenge. Additionally, many farmers were older with a low level of education,which made it more difficult to become aware of the training issues. In some cases, youngfarmers with a degree were working in agriculture, and they were open to participating intraining activities for innovation and knowledge in greenhouse farming systems.

There was a low offer of specific training in greenhouse production systems. The train-ing offered should be related to sustainable agriculture (reduction of consumption andpollutants and increased resilience of exploitation) and precision agriculture technologies.

This work was an investigation on greenhouse farmers’ training. The data providedinformation regarding the needs for greenhouse farmers’ training in smart farming tech-nologies, which are the core of development. The issue has to be further investigated,in relation to the organisation and operation of greenhouse farmers, addressing the differentdimensions of sustainability: environmental, social and economic.

Our findings have significant policy implications in terms of encouraging technologyadoption and spread in Greece. Improving greenhouse technology training could be akey step in encouraging technology acceptance and diffusion; new information lowersproduction costs, boosts farm profits and exposes farmers to new technologies. Agricul-tural training and farm visits have also been found to be favourably connected with theadoption of better farming techniques. Farmers’ participation in such programs, without adoubt, helps to increase their ability and motivates them to replicate such innovations.However, for resource-poor farmers, it is critical to give subsidies and connect them to newtechnologies; our research demonstrated that farmers who receive subsidies and financingare more likely to embrace contemporary technology. Farmers must be provided withthe necessary input packages to increase their rate of adoption of improved technologies,in addition to receiving broad exposure to field demonstrations, farm visits and training.

Author Contributions: Conceptualization, A.K. and V.T.; methodology, A.K., V.T. and A.L.-T.; vali-dation, A.K., V.T., P.B. and A.L.-T.; formal analysis, A.K., V.T. and A.L.-T.; investigation, V.T. and A.K.;data curation, V.T. and N.S.; writing—original draft preparation, A.K. and V.T.; writing—review andediting, A.K. and A.L.-T.; visualization, V.T. and N.S.; supervision, A.K.; project administration, A.K.;funding acquisition, A.K., P.B. and A.L.-T. All authors have read and agreed to the published versionof the manuscript.

Funding: This research received no external funding. This research was conducted in the frameworkof the Next Generation Training on Intelligent Greenhouses (NEGHTRA) project, which is cofundedby the Erasmus+ Programme of the European Union, Code: 621723-EPP-1-2020-1-EL-EPPKA2-KA,https://www.neghtra.eu, accessed on 14 September 2021.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

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Data Availability Statement: The data presented in this study are available on request from thecorresponding author. The data are not publicly available due to containing information that couldcompromise the privacy of research participants.

Acknowledgments: We would like to thank the GeoTechnical Chamber of Greece/Departmentof the Peloponnese, and in particular Athanasios Petropoulos and Christos Thanopoulos for theirsubstantial contribution to the collection of questionnaires from the rural population of Greece.Furthermore, we would like to thank V.J. Moseley for the review, the English language editing andhis valuable comments.

Conflicts of Interest: The authors declare no conflict of interest.

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