Research on Innovative Training on Smart Greenhouse Technologies for Economic and Environmental Sustainability

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 the agricultural sector. In the greenhouse sector, these technologies help farmers increase their profits and crop yields while minimizing the production costs, produce in a more environmentally friendly way and mitigate the risks caused by climate change. In greenhouse farming, especially in the Mediterranean 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 necessary to demonstrate the advantages of innovations related to sustainable agriculture, there is a little opportunity for specific training on greenhouse production in cutting-edge technologies. To gain insight into this problem, questionnaires for greenhouse farmers and intermediaries were developed in multiple choice format and filled in by the stakeholders. A statistical analysis was performed, and the results are presented in graphical form. In most cases, the findings confirmed that producers who run small farms, in most cases, have a lack of knowledge, especially on how to manage climate control systems or fertigation systems. The majority of farmers were elderly with a low level of education, which makes it difficult to be aware of the training issues, due to distrust and a lack of 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 activities and innovation. The most desirable training offer should be related to sustainable agriculture and precision agriculture technologies.

1. Introduction

The current human population growth trend, combined with evolving consumption habits, rising demand and food waste, is putting an unprecedented strain on agricultural systems and natural resources. As a result, one of the biggest problems that humanity will face in the 21st Century is food supply [1,2,3]. In Greece, greenhouse crops are one of the most dynamic primary production sectors, and at the same time, this is a cultivation system to which most precision farming technologies and techniques can be directly applied.

Today, Greece has about 61,000 acres of greenhouses, while climate change in different 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 and the rest of Crete. Respectively, there are 9000 acres in the Peloponnese and 26,000 acres scattered throughout Greece with special reference to Attica and Central Macedonia [4].

Of the total number of installed greenhouse units in Greece, 30% are modern and capable of producing safe high-quality products while ensuring high yields by using modern technologies and automating a large part of their production process. The main reasons for the increase of automation in greenhouses are the ever-increasing production size (creation of 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 produce safe 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 the unsatisfactory integration of new techniques and technologies in the production process [6]. The introduction of systems based on the measurement and management of information can contribute to the optimal management of crops [7]. The modern model of agricultural production 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 concept of precision farming or smart farming, based on the use of Information and Communication Technologies (ICTs) for the precise control of all inputs and accurate output planning in the farming 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 suffered a 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 from the consequences of market instability, as well as the effects of climate change, which exacerbates the frequency and severity of extreme weather events, resulting in reduced crop yields, lost production and a further decline in agriculture income [10,11,12].

In particular, the term precision agriculture refers to an area where sensor technology brings new capabilities that solve old problems. According to a recent report by the Food and Agriculture Organization of the United Nations (FAO), between 2005 and 2015, natural disasters cost USD96 billion in damage to agricultural and livestock production. The FAO argues 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 the resources 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 for 2024 [15].

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

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

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

The present paper is organised as follows. Section 2 describes the sampling, collection and processing of the raw data. Section 3 presents the results of the farmers’ and intermediaries’ questionnaires and the statistical analysis. Finally, Section 4 presents the conclusions of the research.

2. Materials and Methods

2.1. Sampling

The University of Patras and GeoTechnical Chamber of Greece (GeoTEE) designed two open and closed types of questionnaires, addressed to farmers and intermediaries (agriculturalists, farming schools and associations, cooperatives, VET organisations, chambers of commerce and local governments and development agencies) for greenhouses throughout Greece [21,22].

The sampling method was stratified with a division of the farmers’ cooperatives of Greece per region and then sorted by cluster category. The sample number was determined per cluster [23] in combination with the breakdown of cooperative units by regional unit following Bayesian statistical methods [24]. The survey was carried out in the physical presence 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 parameters of training for greenhouse farmers. The key stakeholders reached within their domain 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 were collected. The distribution was from all over Greece, but mainly from areas with a high concentration of greenhouses (Crete, the Peloponnese and Attiki). The questionnaires were disseminated using Google Forms and collected using Google Spreadsheets. The data were gathered and grouped per question.

The data collected included information on the farm, farmer and intermediary characteristics, institutional, production and market-related factors. Socioeconomic and demographic variables of the respondents included the farm household head’s gender, level of education, marital status, household size, occupation, access to climate information and its source, access to credit and income, among others. The full lists of the parameters are given in

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

Variable Type	Variable Name	Variable Explanation
Farmer’s attribute	Type of exploitation $\left(X_1\right)$	Personal/company
	Family members employed in the greenhouse $\left(X_2\right)$	Farmers’ family members employed in the greenhouse
	Gender $\left(X_3\right)$	The gender of the farmer
	Age $\left(X_4\right)$	The age of the farmer
	Education level $\left(X_5\right)$	The level of education
Greenhouse’s attribute	Exploitation region $\left(X_6\right)$	The region where the greenhouse is located
	Agricultural acreage $\left(X_7\right)$	The area of agricultural land
	Main cultivation $\left(X_8\right)$	The main cultivation crop
	Secondary cultivation $\left(X_9\right)$	The secondary cultivation crop
	Other cultivation $\left(X_{10}\right)$	Other cultivation crop
	Cultivation system $\left(X_{11}\right)$	The cultivation system used
	Greenhouse construction $\left(X_{12}\right)$	The greenhouse construction type
	Type of greenhouse $\left(X_{13}\right)$	The type of greenhouse
	Frame materials $\left(X_{14}\right)$	Frame materials
	Cover materials $\left(X_{15}\right)$	Cover material
	Cooling system $\left(X_{16}\right)$	Cooling system
	Heating system $\left(X_{17}\right)$	Heating system
	Energy source $\left(X_{18}\right)$	Energy source
	Irrigation system $\left(X_{19}\right)$	Irrigation system
	Automatic controller $\left(X_{20}\right)$	Is an automatic controller used?
	Pest and disease control system $\left(X_{21}\right)$	Pest and disease control system
	Crop selection criteria $\left(X_{22}\right)$	The criteria by which the farmers decide on the type of crop they will grow the next season in their greenhouse
	Greenhouse technological development classification $\left(X_{23}\right)$	Classification of the greenhouse in terms of technological development
	Preferable new technology $\left(X_{24}\right)$	What new technology could be applied in the greenhouse?
	Agricultural consultant $\left(X_{25}\right)$	If the greenhouse employs an agricultural consultant
	Workers in the greenhouse $\left(X_{26}\right)$	Are there any workers employed in the greenhouse?
	Migrant workers $\left(X_{27}\right)$	If the workers are migrants
	Product distribution $\left(X_{28}\right)$	How is the product distributed?
	Product for export $\left(X_{29}\right)$	If the product is intended for export
	Percentage exported $\left(X_{30}\right)$	What percentage of the product is exported?
	Production certification $\left(X_{31}\right)$	If there is a product certification
	Type of product certification $\left(X_{32}\right)$	Which product certification is used?
Socioeconomic factors	Source of information about technological developments $\left(X_{33}\right)$	How is the farmer informed about the modern technological developments of greenhouse crops?
	Subsidized programmes $\left(X_{34}\right)$	If the greenhouse has been included in subsidized programmes
	Which subsidized programmes $\left(X_{35}\right)$
	Young Farmers Business $\left(X_{36}\right)$	If the farmer has joined the Young Farmers Business programme
	Agricultural training seminars attended $\left(X_{33}\right)$	If the farmer has attended agricultural training seminars
	Thematic areas interested in for training $\left(X_{34}\right)$	Thematic areas farmers are interested in for training
	Training issues $\left(X_{35}\right)$	Training issues faced by farmers
	Skills to be improved during training $\left(X_{36}\right)$	What skills would be improved during training?
	Rating the different forms of training $\left(X_{37}\right)$	Rating the different forms of training
	Appropriate media for training $\left(X_{38}\right)$	What is the most appropriate medium for training?
	Effectiveness of training methods $\left(X_{39}\right)$	What are the most effective training methods?
	Barriers to learning $\left(X_{40}\right)$	What are the barriers to learning for the farmers?

and

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

Variable Type	Variable Name	Variable Explanation
	Crop systems in the area $\left(X_1\right)$	Intermediary’s perception concerning the crop systems in the area
	Integrated crop management systems in the area $\left(X_2\right)$	Intermediary’s perception concerning the integrated crop management systems in the area
	Suggested greenhouse system $\left(X_3\right)$	Which greenhouse system does the intermediary suggest?
	Technological systems in greenhouses in the area $\left(X_4\right)$	What are the technological systems in greenhouses in the area?
	Advanced (cutting-edge) technology systems in greenhouses in the area $\left(X_5\right)$	What are the advanced (cutting-edge) technology systems in greenhouses in the area?
	Level of farmers’ training for the use of already installed technology $\left(X_6\right)$	What is the perceived level of farmers’ training for the use of already installed technology?
	Greenhouse crop technologies to be immediately adopted $\left(X_7\right)$	What are the greenhouse crop technologies that must be adopted immediately?
	Services to farmers $\left(X_8\right)$	What services to the farmers does the intermediary provide?
	Expected effects of greenhouse crops $\left(X_9\right)$	What are the expected effects of greenhouse crops?
	Percentage exported $\left(X_{10}\right)$	What percentage of the production is being exported?
	10-year period cultivation changes $\left(X_{11}\right)$	How often do cultivations change in a 10-year period?
	Factors with greater impact on the country’s economy $\left(X_{12}\right)$	What are the factors with a greater impact on the country’s economy?
	Crop selection criteria $\left(X_{13}\right)$	What are the criteria for the selection of next year’s crop?
	Level of cutting-edge technology adoption $\left(X_{14}\right)$	What is the level of cutting-edge technology adoption?
	Obstacles to cutting-edge technology adoption $\left(X_{15}\right)$	What are the obstacles to the adoption of cutting-edge technology?
	Level of interest in training in thematic areas $\left(X_{16}\right)$	What is the level of interest in training in thematic areas?
	Training problems faced by farmers $\left(X_{17}\right)$	What are the main training problems faced by farmers?
	Degree of advice from consultants $\left(X_{18}\right)$	How much do the farmers listen to the intermediary’s advice?
	Importance of farmers greenhouse training $\left(X_{19}\right)$	What are the most important fields of training for farmers?
	Areas of training that farmers should invest in $\left(X_{20}\right)$	What are the areas of training in which the farmers should invest?
	Level of farmers interest in training programmes $\left(X_{21}\right)$	What is the level of farmers interest in training programmes?
	Skills farmers will improve in training $\left(X_{22}\right)$	What are the skills farmers will improve in training?
	Rating different forms of training for farmers $\left(X_{23a}\right)$	What is the rating of the different forms of training for the farmers?
	Rating different forms of training for intermediaries $\left(X_{23b}\right)$	What is the rating of the different forms of training for the intermediaries?
	Training method effectiveness $\left(X_{24}\right)$	How effective is each training method?
	Barriers to learning $\left(X_{25}\right)$	What are the main barriers to learning?
	Factors that could increase the competitiveness of greenhouse products $\left(X_{26}\right)$	What factors could increase the competitiveness of greenhouse products?
	Obstacles to the increase of greenhouse production $\left(X_{27}\right)$	What are the obstacles to the increase of greenhouse production?
	Future of greenhouse technological development rating $\left(X_{28}\right)$	How is the future of the greenhouse technological development rating perceived?
	Conditions that the farmers will adapt to for new modern/alternative greenhouse crops $\left(X_{29}\right)$	Under what conditions will the farmers adapt to new modern/alternative greenhouse crops?
	Need for collaboration in regional groups/clusters $\left(X_{30}\right)$	Is regional grouping/clustering necessary?

.

The data were summarized into descriptive statistics to provide users with the frequencies of each variable in the dataset. To further help the readers understand for the collected data, suitable measures of central tendency were performed. These measures provide a numerical 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 respondents answered the questions [26]. These measures provided the variability or the amount of change in the data with the most common measure to be the standard deviation, which determines whether a particular data value is close to or far from the mean. The descriptive statistical analyses were performed with IBM SPSS [27], and the results are presented in

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	0.0114
Level 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	−0.6256
Energy 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	−0.6613
Coverage and PV	2.8298	0.1500	1.0283	1.0574	−0.8015	−0.5207
Circular 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	−0.9303
Business 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	−0.2436
National 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	−0.3227
Forms 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	−0.9612
Virtual and blended learning	2.9024	0.1554	0.9951	0.9902	−1.1744	−0.2766
Massive 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	0.1518
Experienced farmers as mentors	2.6170	0.1476	1.0120	1.0241	−1.0004	−0.2024
Knowledge 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	0.0590
Appropriate media for learning	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Presentations	3.1277	0.1312	0.8997	0.8094	−0.6073	−0.6347
Videos	2.7619	0.1438	0.9321	0.8688	−0.9934	−0.0630
Audio	2.4146	0.1674	1.0718	1.1488	−1.1636	0.2331
Text	2.2683	0.1563	1.0006	1.0012	−1.1706	0.0518
Books	2.5106	0.1517	1.0399	1.0814	−1.1338	0.0310
Live 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	0.0831
Efficiency 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	−0.9178
Short-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	−0.7838
Online 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	−0.9365
Online 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	−0.1182
Creating newsgroups/Information in the form of forms/brochures	2.4444	0.1369	0.9184	0.8434	−0.6882	0.2631
Television 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	−0.2357
Helpline instructions	2.0465	0.1629	1.0680	1.1406	−0.8295	0.6418

and

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	0.2539
Cutting-edge technologies’ adoption	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Cutting-edge technologies’ adoption	2.1395	0.0975	0.6392	0.4086	−0.4789	−0.1244
Expected effects of greenhouse cultivations	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Modernization of greenhouses	1.3023	0.0783	0.5134	0.2636	1.1906	1.4342
Innovation in the production and distribution of greenhouse products	1.2791	0.0692	0.4539	0.2060	−1.00645	1.0211
More efficient use of natural resources	1.3023	0.0783	0.5134	0.2636	1.1906	1.4342
Promoting the circular economy for sustainable development and environmental protection	1.5116	0.0963	0.6314	0.3987	−0.2410	0.8473
Contribution to the production of competitive products in domestic and international 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	1.9673
Level of impact of greenhouse cultivations on the country’s economy	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
The percentage of modern greenhouses was higher	1.1860	0.0600	0.3937	0.1550	0.8338	1.6725
Modernization of greenhouses and the use of state-of-the-art technologies acted 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 greenhouses with technological and digital infrastructure	1.4651	0.0902	0.5916	0.3499	−0.1809	0.8645
Organizing into producer groups and certification, combined with offering high-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 the sale 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 research institutes 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 producer groups/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	1.2891
Farmers had a better level of knowledge and information	1.0698	0.0393	0.2578	0.0664	10.7552	3.5010
Crop selection criteria	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
The cultivation proposed by the public bodies	1.1628	0.2155	1.4131	1.9967	−1.7995	0.4413
Cultivation suggested by the agricultural consultant	2.0465	0.1594	1.0455	1.0930	−0.0531	−1.0142
The 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	2.2687
Market demand due to conditions	1.2791	0.0837	0.5488	0.3012	2.7987	1.8841
The usual crops of the area	1.4186	0.0761	0.4992	0.2492	−1.9773	0.3420
A new crop that they saw applied by another producer/Internet	2.0465	0.1410	0.9246	0.8549	−0.3916	−0.6638
Financial incentives for alternative crops	1.7442	0.1492	0.9782	0.9568	−0.9052	−0.2519
Cutting-edge technologies’ adoption obstacles	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Discontinuities in strategic planning issues	2.8605	0.1271	0.8333	0.6944	1.4457	−1.2807
Technical and programming difficulties	2.5581	0.1462	0.9587	0.9192	−0.6614	−0.8534
Inability to borrow from financial institutions	2.9535	0.1881	1.2335	1.5216	−1.0417	−0.7856
Failure to join a national or European funding program	2.5349	0.1833	1.2021	1.4452	−1.5148	−0.3015
Inadequate training in innovation	3.1163	0.1381	0.9053	0.8195	1.2496	−1.2479
Lack of financial incentives	2.8837	0.1637	1.0737	1.1528	−0.4674	−0.8482
Inadequate access to research at higher education institutions	2.3721	0.1969	1.2914	1.6678	−1.7789	0.0185
Lack of synergy with trained scientific staff	2.4651	0.1833	1.2022	1.4452	−1.5809	−0.2159
Lack of technical–scientific teams with broad training	2.0930	0.1877	1.2308	1.5150	−1.6207	0.3778
Decisions with the sole aim of surviving without a long-term plan	3.2558	0.1454	0.9535	0.9092	1.2919	−1.4121
Lack of trust in cutting-edge technology systems	2.3256	0.1990	1.3042	1.7080	−1.7962	0.0983
Interest in thematic areas	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Climate management/technologies/equipment	2.1163	0.1209	0.7931	0.6290	0.5546	0.6866
Energy and resources management	2.7674	0.0989	0.6487	0.4208	−0.6194	0.2627
Automation/greenhouse digitalization	2.7209	0.1385	0.9083	0.8250	−0.4791	−0.4017
Coverage and PV	2.0698	0.1306	0.8562	0.7331	−1.1114	0.1011
Circular economy	3.1628	0.1242	0.8145	0.6633	1.5600	−1.1459
Irrigation–fertigation–biostimulant indigenous AMFs–nutrient analysis	2.7674	0.1044	0.6844	0.4684	−0.7980	0.3324
Business basics, owning/managing a fresh produce business, quality and safety	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	−0.2826
National and international produce trade	2.5116	0.1305	0.8556	0.7320	−0.5250	−0.0378
E-skills	2.5581	0.1214	0.7959	0.6334	−0.3730	0.0979
Training problems greenhouse farmers are facing	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Incomplete offer of specialized programs	2.8837	0.1163	0.7625	0.5814	−0.4730	−0.1360
Limited available time	2.7674	0.1096	0.7184	0.5161	−0.2845	−0.0227
Scepticism about usefulness in cultivation	2.7674	0.1238	0.8117	0.6589	−0.5132	−0.1004
High cost	2.7907	0.1313	0.8607	0.7409	−0.4833	−0.2768
Lack of pilot diffusion programs	3.000	0.0941	0.6172	0.3810	−0.1985	0.0000
Lack of sufficient knowledge to take further training including e-skills	2.8140	0.1165	0.7639	0.5836	−0.5795	−0.0024
Difficulties to manage learning/training software	2.9767	0.1267	0.8306	0.6899	−0.8923	−0.2169
Lack of a culture of change	3.1395	0.1226	0.8042	0.6467	−1.3963	−0.2635
Lack of interest in participation	2.8837	0.1254	0.8226	0.6766	−0.3817	−0.3149
Greenhouse structure limitations	2.3256	0.1090	0.7145	0.5105	0.5053	0.6553
Rating of training forms	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Presentations	3.1395	0.1226	0.8042	0.6467	−0.3884	−0.5515
Videos	3.2093	0.1225	0.8035	0.6456	0.9172	−0.9830
Audio	2.6977	0.1580	1.0359	1.0731	−1.1387	−0.1558
Text	1.9535	0.1243	0.8151	0.6645	−0.6693	0.3641
Books	1.9302	0.1388	0.9101	0.8283	0.3712	0.9372
Live teacher/seminars	3.3488	0.10979	0.7199	0.5183	−0.7856	−0.6428
Training method efficiency	Mean	Standard error	Standard deviation	Sample variance	Kurtosis	Skewness
Classroom sessions/education at the individual level/individual contact	3.3256	0.1233	0.8083	0.6534	0.1502	−0.9592
Short-term seminars/lectures at physical meetings	3.1860	0.1211	0.7945	0.6312	−0.1817	−0.6510
Practical courses/exercises	3.5116	0.1171	0.7676	0.5891	1.75545	−1.5317
Online courses (e-learning)	2.7442	0.1204	0.7896	0.6235	−0.7933	0.1936
Agriculturalist’s farms visits/educational excursions	3.5116	0.1122	0.7359	0.5415	−0.0855	−1.1698
Online communication with agriculturalist (real time)	2.8605	0.1314	0.8614	0.7420	−1.1816	0.0454
Creating newsgroups/information in the form of forms/brochures	2.4651	0.1425	0.9348	0.8738	−0.7687	0.1977
Television broadcasts/broadcasts on radio/articles in newspapers	2.6047	0.1417	0.9294	0.8638	−0.7102	−0.2275
Agricultural journals	2.4884	0.1262	0.8273	0.6844	−0.4210	0.1710
Helpline instructions	2.2093	0.1354	0.8880	0.7885	−0.3849	0.4213

[28,29].

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

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

3. Results and Discussion

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

The main problem is that many of the farmers were old and had a low level of education, which made it more difficult to be aware of training opportunities, and they presented 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 reliable information and little data on the market assuming great production risk. Therefore, their strategy was usually survival with important cost control. Recently, young graduates have been 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 worth mentioning that younger farmers were more educated than older farmers, and they were mainly 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 cultivations being ornamentals (flower crops) and strawberries (Figure 5). In addition, the main criterion by which farmers decided on the type of crop they would grow the next season in the greenhouse corresponded to a commercial agreement that had been concluded or the needs of the market. A secondary criterion was the judgement of the farmer or the usual cultivation in the wider area (Figure 6).

Multispan greenhouses are the most common type in Greece (Figure 7). Most farmers classified 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 of producers or a cooperative (Figure 9).

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

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

3.2. Intermediaries

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

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

The intermediaries estimated that the contribution to the production of competitive products in domestic and international markets, increasing agricultural income and improving 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 impact on the country’s economy if farmers had a better level of knowledge, and information and modernization of greenhouses and the use of state-of-the-art technologies acted as incentives for young entrepreneurs (Figure 19).

The intermediaries suggested that the main criteria by which farmers decided on the type of crop they would grow in their greenhouse were crops that corresponded to a commercial 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-term plan and inability to borrow from financial institutions were the main obstacles to the adoption of cutting-edge technologies by greenhouse farmers (Figure 21).

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 manage learning/training software were the basic training problems faced by greenhouse farmers (Figure 23).

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

4. Conclusions

Farmers’ digital and environmental skills are very important for successful greenhouse farming. Producers who run small farms, in most cases, lack knowledge, especially on how 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 process can affect greenhouse farming. The main issue in innovation uptake by greenhouse farmers was 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 the necessary guarantees. Therefore, their strategy was usually survival with significant cost control. On the other hand, the technical training of farmers on control and the automation of processes in the greenhouse (precision agriculture) was very basic. Finally, the lack of qualified personnel able to adopt new knowledge was a key factor.

The main issue in the technology adaptability of greenhouse farmers was the lack of technical training and distrust. In general, greenhouse farms did not employ enough qualified personnel as most of the farms were small, and finding the free time for training actions was 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, young farmers with a degree were working in agriculture, and they were open to participating in training activities for innovation and knowledge in greenhouse farming systems.

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

This work was an investigation on greenhouse farmers’ training. The data provided information regarding the needs for greenhouse farmers’ training in smart farming technologies, 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 different dimensions of sustainability: environmental, social and economic.

Our findings have significant policy implications in terms of encouraging technology adoption and spread in Greece. Improving greenhouse technology training could be a key step in encouraging technology acceptance and diffusion; new information lowers production costs, boosts farm profits and exposes farmers to new technologies. Agricultural training and farm visits have also been found to be favourably connected with the adoption of better farming techniques. Farmers’ participation in such programs, without a doubt, 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 new technologies; our research demonstrated that farmers who receive subsidies and financing are more likely to embrace contemporary technology. Farmers must be provided with the 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.