Training Sources and Preferences for Agricultural Producers and Professionals in Middle-North Mato Grosso, Brazil

Abstract

Brazil’s midwest has rapidly expanded large-scale commodity crops such as soybeans and maize. We surveyed both agricultural producers and agricultural professionals in the middle-north region of Mato Grosso state, Brazil. Agricultural professionals provide technical support to agricultural producers and both are served by Assistência Técnica e Extensão Rural (ATER) with nationally and internationally recognized extension outreach. Our objectives were to define and contrast agricultural producer and professional characteristics, especially source(s) relied upon for agricultural training. There were 94 agricultural producers and 89 agricultural professionals that responded to our surveys, which were summarized and contrasted using statistical software. There was a predominance of male farmers, married with a broad age range. Agricultural professionals who advise producers had a high educational level. Producers and professionals were most reliant on private sector agricultural companies and business support organizations for agricultural training, versus public institutions such as universities and state/federal agencies. In the state of Mato Grosso, extension outreach can involve joint efforts by public and private sector entities. However, more targeted efforts are needed to ensure that public sector research is more equally used by agricultural producers and professionals in the region, especially during field days and face-to-face technical lectures during the off-season.

1. Introduction

The Cerrado–Amazon ecotone is a macro-region composed of sixteen municipalities with relief, soil, and climate conditions that are favorable to the cultivation of annual crops. According to the Instituto Mato-Grossense de Economia Agropecuária (IMEA) in 2017 [1], this is an important region for grain production and distribution in the state of Mato Grosso and for the Brazilian economy. Technological innovations in Mato Grosso have quadrupled agricultural production from 1990 to 2020, while land area used to grow grain has only increased by 68% in the same period [2]. Brazilian agribusiness development, especially in the state of Mato Grosso, has seen increased demand for qualified labor and professional skills and competencies. Brazil’s federal government has stimulated agricultural development in middle-north Mato Grosso state since the 1970s and 1980s. Migrants from southern Brazil (e.g., Parana state) were encouraged to occupy the Cerrado (i.e., savannah) and Amazon, expanding cattle ranching and crop production [3,4].

Mato Grosso is a state located in the midwest region of Brazil spanning 903,207 km². In 2021, it had 3,567,234 people with a population density of 3.94 inhabitants/km² [5]. The state of Mato Grosso has the largest cattle herd in the country, with 32,424,958 heads. In 2017, Mato Grosso state had 118,679 rural properties totaling 54,922,850 hectares intended for agricultural production [6]. Mato Grosso is the largest Brazilian soybean-producing state, having 10,909,400 hectares planted producing 39,961,100 metric tons (t), with a productivity of 3.663 t/ha in 2022. For maize in the year 2022, the state of Mato Grosso had a planted area of 6,547,400 hectares producing 41,620,100 t with a productivity of 6.357 t/ha [7].

To increase productivity gains in agricultural regions, independent extension projects such as those developed by universities are very important [8]. However, despite the potential for agricultural technological innovation in the region and access to the Universidade Federal de Mato Grosso (UFMT) and its partners, there has been low participation in extension programs (innovation programs, technical lectures, and workshops) offered by these institutions. AgriSciences at UFMT is a program responsible for applied research and rural extension programming (

Table 1. AgriSciences program components in middle-north Mato Grosso state, Brazil.

AgriSciences Program Components	Description	Methods	Participants (2019–2022)
Demonstrative Unit (DU)	Commercial rural property, where research and extension actions are already established. Offers producers and technicians local training programs for practical application of technologies and develops and evaluates research on advantages and disadvantages of such technologies.	Field day Technology showcase Course Technical visit Lecture Meeting	2261
Multiplier Unit	Rural property close to DU disseminating and transferring one or more technologies developed and/or validated at the DU. Promotes technology adoption and enables exchange of experiences between producers. Disseminates knowledge in environment close to their familiar daily life.	Course Technical visit Lecture Knowledge exchange Meeting	33
Community Engagement	Community formed by academics, rural producers and professionals, young people, and rural children. Intended to bring together younger people and rural dwellers to disseminate academic knowledge, scientific production, and technological innovation using correct approaches.	Workshop Lecture Course Seminar	1301
Exchange of Leaders	International, practical experiences by academics and recent graduates. Allows exchange of rural youths, higher education students, and graduates in the United States. Focus on practical teaching of new practices for Mato Grosso.	Course Hands-on Internship	54

) focused on sustainable agriculture established in 2016 [9]. Through public/private partnerships, AgriSciences develops extension actions focusing on producers, technical professionals, and rural youth. From 2019 to 2022, more than 3600 people were directly reached through extension activities (Table 1).

There are numerous challenges to establishing successful agricultural training programs even with a lot of extension agents per capita [10]. It can be difficult for rural agricultural extension to customize education to different types of producers (e.g., row crop, cattle ranchers, subsistence farming) with different socio-economic backgrounds [11]. Previous studies in developing nations suggest more limited participation in agricultural training for older, less educated farmers living further away from extension services [12,13]. Agricultural producers also have limited time and resources to devote to agricultural training. A previous study [14] surveyed 355 farmers in northern Greece where 47.6% could attend less than 2 days of Agricultural Education Programs annually. On-farm demonstrations require extensive and multi-faceted planning [15]. More modern methods of rural extension can be novelties, both for agricultural producers and professionals and institutions in Brazil [16].

While production challenges, mainly in grain production, have encouraged new investments and increased technology diffusion in agriculture [17], addressing environmental and community sustainability can remain challenging. Brazil has had challenges successfully developing agro-ecological extension approaches for agriculture at a federal level [18]. Agricultural education has been shown to be more challenging for environmental management in New Zealand [19] and for rural development policies in Italy [20]. For Peruvian dairy farmers, private agricultural advisors tend to increase farmer reliance on the external inputs these advisors are selling [21]. Acknowledging farmers’ rational barriers to adopting more environmentally friendly practices [22] and engaging them in developing individualized solutions can improve sustainability [23].

Due to the middle-north region of Mato Grosso’s relevance for agriculture and importance in contributing to Brazil’s national and international economy, it is important to better understand farmers’ and agricultural professionals’ sources and preferences for agricultural training. This can reveal potential drivers of farmers’ low level of interest in participating in public innovation programs. Addressing these shortfalls in program interest can meet the goal of enhancing the engagement of agricultural producers and professionals in this region in Brazil.

Thus, there were three general research objectives of our study. The first objective was to develop baseline profiles of crop farmers and agricultural professionals in middle-north Mato Grosso. Our second objective was to identify the sources of and preferences for public and private agricultural knowledge and education by both agricultural producers and professionals in this region. Finally, our third research objective was to determine the extension method (e.g., field day, course, workshop, seminars, etc.) that our audience of farmers and professionals prefers in order to receive such knowledge and education. Customizing agricultural training to better meet the preferences and needs of producers and professionals can improve agricultural production practices and cropping system management in the long run.

2. Materials and Methods

2.1. Study Area and Methodological Approach

The area selected for study was the middle-north macro-region of Mato Grosso state (MT) because of its central location within this state in Brazil. The region is important for the production, marketing, and distribution of commodity crops (e.g., soybeans, maize, and cotton) in MT [1,2]. Our study used qualitative, quantitative, and descriptive data and analyses using the participatory rural appraisal (PRA) methodological approach. PRA is a research and data collection process intended to include the perspectives of all groups of interest of a community, causing a change in the traditional surveyor/surveyed roles so that multiple stakeholders participate in determining how data collection will be conducted [24]. The method also serves as a communication channel between those who share a common problem. We solicited feedback from agricultural producers and professionals to refine our surveys (Supplementary Materials) as well as collection of survey data.

2.2. Sampling and Survey Administration

Our research team surveyed 94 medium-to-large grain producers and 89 agricultural professionals during the spring and early summer of 2019. Producers were mainly from the cities of Nova Mutum (13.82681 S, 56.07165 W), Lucas do Rio Verde (13.073898 S, 55.91885 W), Sorriso (12.54209 S, 55.72081 W), and Sinop (11.85984 S, 55.50723 W), which lie on Route 163, which is among the larger cropping areas in South America. The middle-north region of Mato Grosso transitions from the Cerrado to Amazon biomes moving south to north from Nova Mutum to Sinop (Figure 1). Our survey design, implementation, and analyses are summarized in Figure 2.

Surveys of agricultural producers and professionals were administered both in person and online, using the Google Forms platform. Survey questions were designed to take around half an hour to complete. A team of researchers and graduate students from the Universidade Federal Mato Grosso (UFMT), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), and the University of Minnesota revised survey questions before final administration. We used the “Needs Assessment Circuit” (NAC) [25] which estimated time needed to answer survey questions and number of target responses for each town. NAC was selected based on its use in previous studies of agricultural stakeholder involvement [26]. Surveys of agricultural producers and professionals started during the spring of 2019. Due to low initial response to our online survey after a month, we directly surveyed both agricultural producers and professionals via unions and retailers over three months from June to August 2019.

Our project was presented to the trade unions of each town to facilitate communication to our target audience of agricultural producers. Producers answered questions on paper and sometimes by phone. Support of rural trade unions helped ensure at least two working days were available for direct contact with producers. Our team visited all four cities (Figure 1) and coordinated with agricultural companies to forward e-questionnaires to interested agricultural professionals and their teams. Presentations about our survey were given to at least ten agricultural companies in each city. This facilitated survey data collection in person using paper surveys and, in some situations, by using the online version of the survey and quick communication applications (WhatsApp^®).

Our agricultural producer survey was divided into three sections. The first section included socio-demographic questions on age, gender, marital status, educational level, and residence. Additionally, respondents were asked about their length of experience or service in the agricultural sector. Agricultural producers were asked if they lived on-farm and, if not, how often they visited their farms, and who was responsible for the farm’s technical decisions. The survey’s second section asked questions on farm area, crop production and management, and participation in farm programs and agricultural training. Finally, questions in the third section asked producer preferences for agricultural extension topics, extension methods (e.g., field day, course, workshop, seminars, etc.), as well as general comments and suggestions.

The agricultural professional survey was also divided into three sections. The first section asked about the professionals’ general background such as where they were located, education, occupation, number of farmers served, and crop area and types covered. The second survey section involved technology transfer to clients, probability of using information through current communication channels, frequency of reading specialized literature, and the amount of training carried out in a year. The final survey section asked about preferred extension methods, outreach duration and scheduling, and availability for in-person and online training. General suggestions and comments were also solicited.

2.3. Data Organization and Analysis

Survey data were entered into Microsoft^® Excel 2013 to generate tables and graphs. We coded survey responses to categorical questions in order to generate summary statistics and to use for multi-variate regression analyses [27]. Both agricultural producers and professionals specified the crop systems with which they were involved. Responses to questions commonly asked to both agricultural producers and professionals were tested for statistically significant differences between these two respondent groups using the Z-test for significant differences between proportions in open-source R. These common questions included residence, education, and source(s) used for agricultural training. We arranged open-ended responses for comments and suggestions as a running text and then built a cloud of words using the Atlas.ti^® software. This program identified frequently used words by both producers and professionals for visual comparison. We ran Ordinary Least Squares (OLS) regressions in both open-source R and Palisade Decision Tools Suite’s StatTools to determine independent variables significantly impacting the proportion of public (versus private) training sources for both agricultural producers and professionals.

3. Results

3.1. Survey Respondent and Farm Area Characteristics

3.1.1. Agricultural Producers and Their Farms

Characteristics of agricultural producers are presented in

Table 2. Characteristics for crop producers from middle-north region, Mato Grosso state, Brazil in 2019.

Producer Characteristic	n	%
Age (n = 93):
<25	5	5.4
25–35	22	23.6
36–45	18	19.4
46–55	18	19.4
56–65	23	24.7
>65	7	7.5
Gender (n = 93):
Male	85	91.4
Female	8	8.6
Civil Status (n = 92):
Single	14	15.2
Married	76	82.6
Divorced	2	2.2
Undergraduate area (n = 40):
Agronomist	20	50.0
Other professions	15	37.5
Administrator	2	5.0
Veterinarian	1	2.5
Economist/Accountant	2	5.0
Live on farm (n = 93):
Yes	29	31.2
No	64	68.8
Administrative decisions made (n = 89):
Individually	26	29.2
With husband (wife)	15	16.9
With husband (wife) and children	23	25.8
With someone outside the family	22	24.7
Outside the family	3	3.4
Years in agriculture (n = 93)
0–10	16	17.2
10–19	20	21.5
20–29	23	24.8
30–39	20	21.5
40–50	11	11.8
>50	3	3.2
Farm area managed (hectares) (n = 93):
<500	21	22.6
500–1499	32	34.4
1500–3499	20	21.5
3500–9999	19	20.4
>10,000	1	1.1
Uses farm credit (n = 93):
Yes	76	81.7
No	17	18.3

. About one-quarter of producers (24.7%) were between 56 and 65 years old, while those aged 25 to 35 years comprised 23.6% of the sample. Most producers were male (91.4%) and were married (82.6%). Based on a Brazilian agricultural and livestock censuses conducted between 2006 and 2017, the cultivation of cotton, sugarcane, seeds and seedlings, planted forests, and aquaculture had minimal participation from women. These activities employ poorly paid wage labor, are highly mechanized, and are common on patriarchal farms [28].

Most surveyed producers (61.3%) reported having ≥20 years of farming experience (Table 2). Agricultural producers had a range of educational backgrounds, where 46.7% had up to a high school degree and 27.2% had an undergraduate college degree (

Table 3. Characteristics and preferences of agricultural producers and professionals in middle-north region, MT, Brazil in 2019.

Respondent Characteristics	Producers		Professionals		Z-Test
and Preferences	n	% resp.	n	% resp.	p-Value
Location:	91		89
Sinop	19	20.9	29	32.5	0.108
Sorriso	23	25.3	28	31.5	0.450
Nova Mutum	16	17.6	12	13.5	0.580
Lucas do Rio Verde	9	9.9	9	10.1	>0.999
Other	28	30.8	11	12.4	0.005
Educational level:	92		89
Up to high school	15	16.3	2	2.3	0.003
Completed high school	28	30.4	1	1.1	<0.0001
Technical education	3	3.3	14	15.7	0.009
Other higher education	15	16.3	0	0	0.0002
College Degree (B.A., B.S.)	21	22.8	47	52.8	<0.0001
Graduate Degree (M.S., Ph.D.)	6	6.5	25	28.1	0.0003
Works with crop system type(s) a:	93		85
Just soybeans (S)	6	6.5	1	1.2	0.155
S-Maize (M)	55	59.1	56	65.9	0.439
S-Cotton	7	7.5	28	19.7	<0.0001
S-M-Pasture	28	30.1	26	18.3	>0.999
Other crop(s)/enterprise	31	33.3	31	21.8	0.778
Sources for agricultural training a:	83		86
Public training sources:
Regional extension	39	47.0	22	25.6	0.006
Universities	10	12.0	16	18.6	0.333
State research	7	8.4	6	7.0	0.947
Federal research	21	25.3	18	20.9	0.623
Private training sources:
Chemical companies	37	44.6	71	82.6	<0.0001
Fertilizer companies	17	20.5	55	64.0	<0.0001
Retail companies	40	48.2	44	51.2	0.816
Independent consultants	15	18.1	29	33.7	0.032
Other private entities	3	3.6	4	4.7	>0.999
Preferred topics for training a:	90		88
Physical soil qualities	40	44.4	44	50.0	0.554
Soil conservation	47	52.2	40	45.5	0.451
Soil–plant–atmosphere relations	26	28.9	17	19.3	0.188
Soil preparation	38	42.2	48	54.5	0.135
GPS and precision agriculture	35	38.9	34	38.6	>0.999
Weed management	55	61.1	63	71.6	0.187
Plant mineral nutrition	42	46.7	61	69.3	0.004
Correcting soil acidity	39	43.3	53	60.2	0.035
Plant fertilizer management	50	55.6	64	72.7	0.026
Soil biology	49	54.4	37	42.0	0.132
Soil fertility assessment	49	54.4	62	70.5	0.040
Other topics	10	11.1	8	9.1	0.843
Optimistic on future of agriculture:	91		88
Yes	68	74.7	83	94.3	0.0007

^a Producers and professionals could specify multiple categories.

). Of the 40 producers specifying other higher education or an undergraduate degree, 50% were agronomists (Table 2). Advanced degrees (6.5%) and technical education (3.3%) comprised the remaining balance of educational backgrounds (Table 3). Our results are consistent with a previous study reporting that rural producers in Brazil differed with regard to schooling and how technical information is obtained [29].

Most interviewed producers (68.8%) did not live on-farm (Table 2) and were more likely to manage farms located in areas outside of the four major cities compared to the farms serviced by agricultural professionals (p = 0.005, Table 3). Typically, producers maintain a residence in an urban area, which allows access to a better living standard for their families (e.g., education, healthcare, entertainment, commerce, and other services). Finally, management decisions were made individually by the producer (29.2%) or were made together with some member of the family (42.7%) or with an external individual (24.7%). Thus, 96.6% of producers’ agricultural property and enterprises are managed by the farmer, his wife, and children (Table 2).

Most farms managed by agricultural producers fell in the 500-to-1499-hectare (ha) size class (34.4% of producers), with 77.4% of the farms greater than or equal to 500 ha (Table 1). Most farms managed in our study were larger than observed in other regions of Brazil where only 0.9% of the farms are over 1000 hectares [30]. Mato Grosso is Brazil’s largest agricultural state, producing 21.1% of the country’s agricultural value [31]. Commodity crops include maize (Zea mays L.) or cotton (Gossypium sp.) grown as a second crop following soybeans (Glycine max L.) in the same production year [32]. Commodity crop production has historically transitioned from frontier development to greater intensification in order to reduce deforestation through collaboration of the private and public sectors with non-governmental organizations (NGOs) [33].

3.1.2. Agricultural Professionals and Advisory Area

Agricultural professionals were statistically more highly educated than producers, with 52.8% being college graduates, 28.1% having a post-graduate degree, and 15.7% completing technical education beyond high school. Only 3.4% had some high school education or just a high school diploma (Table 3). Other research has highlighted the need for educated workers since they have to perform different jobs in the urban or rural agribusiness sector [34].

Table 4. Characteristics of agricultural professionals from middle-north region, Mato Grosso state, Brazil in 2019.

Professional Characteristics	n	%
Number of rural producers assisted (n = 89):
0–19	39	43.8
20–39	30	33.7
40–60	4	4.5
>60	16	18.0
Total area covered by professionals’ work (n = 88):
0–19,000 hectares	24	27.3
20,000–39,999 hectares	28	31.8
40,000–59,999 hectares	14	15.9
>60,000 hectares	22	25.0

summarizes responses to questions specifically asked to agricultural professionals. Here, 43.8% of professionals provided assistance to 0 to 19 clients, 33.7% to 20 to 39 clients, and 18% to more than 60 clients. This was similar for farm area serviced with 59.1% of professionals being responsible for under 40,000 hectares (ha) and 25% for more than 60,000 ha. Large-scale soybean production is one of the most economically important crops in Brazil and crop extension plays a critical role in promoting scientific advances and new production technologies [35].

3.2. Agricultural Systems

Based on the responses of agricultural producers and professionals regarding the crop(s) that they work with, five annual cropping systems were defined: (1) soybeans (S) only, (2) S-maize (M), (3) S-cotton, (4) S-M-pasture, and (5) systems involving other crops/enterprises. The S-M double-cropping system had the highest involvement from both producers (59.1%) and professionals (65.9%). For producers, this was followed by other crops/enterprises, S-M-pasture, S-cotton, and just soybeans. Professionals were similar, with the exception of S-cotton being more commonly worked with than the S-M-pasture integrated system. Professional involvement with S-cotton was statistically greater (p ≤ 0.0001) than producers (Table 3), which may be due to the management complexity of cotton [36] requiring more agricultural professional advisory services.

Most producers plant one, two, or three annual crops in a year, typically following each other from the wet season (October to February) to dry season (March to September). Only 6.5% of producers and 1.2% of professionals are specialized in just soybeans. More common are involvement in either (1) S-cotton due to longer maturity for cotton or (2) S-M where a third sequential crop could be added, such as S-M-pasture, where the pasture is under-seeded during maize planting in February. Most producers (89.2%) and professionals (84.2%) were involved with soybeans double-cropped with maize as a second crop (safrinha). Fewer producers (33.3%) and professionals (21.8%) dealt with second/third crops such as bean, sorghum, and Crotalaria juncea (rattle pods grown as a cover crop). Furthermore, 30.1% of farmers cultivated pasture, typically as the third crop, while 18.3% of professionals worked with this type of integrated cropping system (Table 3). Third crops grown during the dry season (June to September) with no or very limited rainfall require more technology (e.g., irrigation), typically used by larger producers [37].

3.3. Contrasting Use of Public versus Private Agricultural Training

Table 3 compares surveyed agricultural producers and professionals with respect to their sources for public or private agricultural training. Public sources of agricultural training include regional extension (e.g., Fundação MT, IMA, SENAR-MT), universities (e.g., UFMT), state research (e.g., EMPAER, INDEA), and federal research (e.g., Embrapa, MAPA) entities (

Table 5. Public and private agricultural training entities and 2021 budgets in Brazil.

Type	Class	Abbreviation	Entity Name	2021 Budget (USD/Year)
				International	Brazil	Mato Grosso	Source
Public	Regional extension	Fundação MT	Fundação de Apoio à Pesquisa Agropecuária de Mato Grosso	-	-	n/a	-
		IMA	Instituto Matogrossense de Algodão	-	-	n/a
		SENAR-MT	Serviço Nacional de Aprendizagem Rural de Mato Grosso	-	-	25,756,833	[38]
	Universities	UFMT	Universidade Federal de Mato Grosso	-	-	180,261,746	[39]
			AgriSciences	-	-	169,523
	State	EMPAER-MT	Empresa Mato-Grosssense de Pesquisa, Assistência e Extensão Rural	-	-	30,468,664	[40]
		INDEA	Instituto de Defesa Agropecuária do Estado de Mato Grosso	-	-	43,059,919	[40]
	Federal	Embrapa	Empresa Brasileira de Pesquisa Agropecuária	-	657,227,049	n/a	[41]
		MAPA	Ministério da Agricultura, Pecuária e Abastecimento	-	787,328,902,333	n/a	[42]
Private	Chemicals	-	Bayer (includes Monsanto)	-	2,041,688	n/a	[43]
		-	Corteva (includes Pioneer)	15,655,000	n/a	n/a	[44]
	Fertilizers	-	Mosaic	1,630,600,000	n/a	n/a	[45]
		-	Yara	384,000,000	n/a	n/a	[46]
	Retailers	-	Agroamazônia	-	n/a	n/a	-
		-	Agroinsumos	-	n/a	n/a	-
	Independent	-	Independent Consultants	-	n/a	n/a	-
	Other	-	Other private entities	-	n/a	n/a	-

). Private sector training sources were companies making agricultural chemicals (e.g., Bayer, Syngenta, Pioneer), fertilizers (e.g., Mosaic, Yara), retailing agricultural inputs (e.g., Agroamazônia, Agroinsumos), independent consultants, and other private entities (Table 4). Agricultural producers were more likely to rely on regional extension (p = 0.006) compared to professionals.

However, professionals were significantly more dependent on chemical and fertilizer companies (p ≤ 0.0001) as well as independent consultants (p = 0.032) for agricultural training compared to producers (Table 3). Of the 83 producers and 86 professionals that answered our question on source(s) of agricultural training, less than half of each group relied on public sources (7% to 47%). Less than half of producers relied on private sources as well (3.6% to 48.2%, Table 3). Over half of surveyed professionals relied on companies producing or retailing agricultural inputs (51.2% to 82.6%, Table 3).

These private entities had greater budgets on average compared to regional/state-level public entities. Private entities had budgets ranging from USD 15,655,000 to 1,630,600,000, while regional/state-level public sources only had budgets of USD 25,756,833 to 180,261,746 (Table 5). For the Universidade Federal de Mato Grosso (UFMT), this is the entire university system budget, where most resources are not directed to farmer outreach unlike the Serviço Nacional de Aprendizagem Rural de Mato Grosso (SENAR-MT), Empresa Mato-Grosssense de Pesquisa, Assistência e Extensão Rural (EMPAER-MT), and the Instituto de Defesa Agropecuária do Estado de Mato Grosso (INDEA). For example, the AgriSciences program at UFMT, which conducts direct outreach to area farmers, has an average annual budget of USD 169,523, of which only about USD 11,808 is directly from the UFMT university system.

Our results are comparable with Carbonera et al. 2020 [47], who found that agricultural producers in Santa Rosa, Rio Grande do Sul state in southern Brazil relied upon both private and public sources for agricultural training. Here, farmers were technically assisted by agricultural input suppliers, finance and other professionals, and private cooperatives. Public entities included the Association of Technical Support and Rural Extension Enterprises in Rio Grande do Sul (Associação Riograndense de Empreendimentos de Assistência Técnica e Extensão Rural—EMATER/RS) and City Administration. Producers also used non-governmental organizations (NGOs) for training. Increased producer demand for more technical assistance was observed in some of the relationships examined.

Agricultural producers had a lower number of responses (481) compared to professionals (594) regarding preferred topics for agricultural training. Word clouds of open-response questions answered by both agricultural producers and professionals are shown in Figure 3. Agricultural producers emphasized “soil” versus “management” for professionals. The three words most often mentioned by agricultural producers were soil, field, and research (Figure 3a). Agricultural professionals most often used the words management, soil, field, and producers (Figure 3b). Greater emphasis on “management” was consistent with professionals having statistically greater preferences for preferred topics for training having to do with more technical aspects of crop management, such as plant mineral nutrition (p = 0.004), fertilizer management (p = 0.026), soil fertility assessment (p = 0.040), and correcting soil acidity (p = 0.035, Table 3).

Ordinary Least Squares (OLS) regression results for both agricultural producers and professionals explaining the proportion of public training used (dependent variable) are shown in

Table 6. Ordinary Least Squares (OLS) regression for proportion public training for both agricultural producers and professionals from middle-north region, Mato Grosso state, Brazil in 2019.

Proportion Public Training	Producers			Professionals
OLS Model: Independent Variables	Coefficient	Standard Error	p-Value a	Coefficient	Standard Error	p-Value a
Constant	0.65826	0.25337	0.0115 **	0.56507	0.20079	0.0063 ***
Private training source:
Chemical companies	−0.20100	0.07393	0.0083 ***	−0.35957	0.07259	<0.0001 ***
Resale companies	−0.27186	0.07207	0.0003 ***	−0.05143	0.05403	0.3444
Residence:
Sinop	−0.29254	0.10318	0.0060 ***	−0.09202	0.06498	0.1554
Sorriso	0.03919	0.08784	0.6569	−0.16216	0.06462	0.0144 **
Socio-demographics:
Female	0.32599	0.13429	0.0179 **	– b	– b	– b
Single	0.22590	0.09779	0.0239 **	– b	– b	– b
Years of education	−0.00565	0.01277	0.6595	−0.00854	0.01233	0.4907
Agricultural background c:
Managed area (ranges)	−0.00002	0.00001	0.0725 *	−0.000002	0.000001	0.0801 *
Soybeans only	−0.30665	0.19923	0.1284	−0.34075	0.24407	0.1670
Number of crops	0.01103	0.02657	0.6795	0.06672	0.02151	0.0028 ***
Years in agriculture (range)	0.00192	0.00296	0.5178	– a	– a	– a
Number articles read/year	– b	– b	– b	0.00032	0.00018	0.0812 *
Model summary:
Sample size (n)	94			91
Degrees of freedom (df)	11			9
R-squared (R2)	0.4159			0.4631

^a Denotes significance at confidence level (α) of 0.10 (*), 0.05 (**), and 0.01 (***). ^b Independent variable question not asked. ^c For producers, this is managed by them, while for professionals this is related to the producers that they consult.

. Years of education, years in agriculture, number of crops grown, and those specializing in only soybeans were not significant in explaining use of public (versus private) training sources. For producers, years spent in agriculture and being from the city of Sorriso were also not significant. Agricultural professionals who received training from private companies selling agricultural inputs (resale) did not have any significantly different proportion of public training used compared to other professionals. Like agricultural producers, years of education was also not significant. Agricultural professionals that helped producers specializing in soybeans and that received training from resale companies (i.e., distributors) were not more or less likely to use more public training sources (Table 6). All other variables surveyed from both agricultural producers (Table 2 and Table 3) and agricultural professionals (Table 3 and Table 4) were not significant in influencing use of public training sources.

Agricultural producers that had significantly higher proportions of public training sources were less likely to receive training from chemical and resale companies, but they were more apt to be from the city of Sinop, female, and single. Producers that managed less area were more likely to use more public training sources, through this was marginally significant (α = 0.10). Similarly, agricultural professionals that used more public training sources were less likely to receive training from private chemical companies and be from the city of Sorriso. Agricultural professionals that used public training sources significantly read more technical articles per year, consulted on a larger number of crops, and managed less agricultural area (Table 6).

3.4. Financial and Technical Assistance for Producers

As summarized in

Table 7. Producer use and preferences for financial and technical services in middle-north region, Mato Grosso state, Brazil in 2019.

Producer Characteristics and Preferences	n	%
Rural credit:
Rural credit used (n = 93):
Yes	76	81.7
No	17	18.3
Type a (responses = 129):
Banco do Brasil (FCO, etc.)	54	41.9
SICREDI	50	38.8
ABC	2	1.6
Other type(s)	8	6.2
Barter	15	11.6
Technical assistance
Receives technical assistance (n = 91):
Yes	76	83.5
No	15	16.5
Source(s) a (responses = 176):
Regional extension (SENAR)	21	11.9
Universities	5	2.8
State research (EMPAER)	2	1.1
Federal research (Embrapa)	14	7.9
Private companies	58	33.0
Producer associations	45	25.6
Rural unions	29	16.5
Non-Government Organizations (NGOs)	1	0.6
Other sources	1	0.6
Preferences a (responses = 99):
Understanding cost–benefit analysis	37	37.4
New technology and science	28	28.3
Basic technical knowledge	21	21.2
Communication skills	8	8.1
Other	5	5.0
Preferred extension methods a (responses = 245):
Field days	74	30.2
Fairs and exhibitions	45	18.4
Presentations	71	29.0
Congresses and seminars	48	19.6
Other types	5	2.0
None	2	0.8

^a Producers could specify multiple categories.

, most surveyed producers use rural credit (81.7%) and technical assistance (83.5%) to support their farms. Banco do Brazil and SICREDI combined to make up 80.7% of responses for sources of rural credit, with bartering (11.6%), other types (6.2%), and Agricultura de Baixo Carbano (ABC) or the Low-Carbon Agriculture program (1.6%) making up the difference (Table 7). ABC is used by farmers in Brazil who are implementing sustainable agriculture, such as low-carbon practices [48].

Responses to sources of technical assistance were led by private companies (33%), followed by other private entities such as producer associations (25.6%) and rural unions (16.5%). Public sources of technical assistance (SENAR, Universities, EMPAER, and Embrapa) only totaled 23.7% of responses. Preferred technical assistance topics were cost/benefit (37.4%), new technologies/science (28.3%), and basic technical knowledge (21.2%) (Table 7).

Producers indicated a preference for presentations with social gatherings, exchange of experiences, and emphasis on practical dynamics in a familiar environment. Preferred agricultural extension formats as a percentage of responses were field days (30.2%), presentations (29%), congresses or seminars (19.6%), and fairs/exhibitions (18.4%) (Table 7). From October 2016 to May 2022, AgriSciences at the University Federal de Mato Grosso has organized numerous presentations, four field days (2019–2022), and one international congress (2016). There were 538 people that presented and/or attended the VIII SIMBRAS (Simpósio Brazileiro de Agropecuária Sustentável) from 6 to 8 October 2016 [49]. From February 2019 to December 2021, AgriSciences held 56 training events involving a total of 3649 people.

A total of 263 + 786 + 275 = 1324 people attended the first, third, and fourth Agricultural Field Days (2019, 2021–2022). Even though the second live event was canceled in 2020 due to COVID-19, more than 800 people downloaded the inaugural book for this [50], with book publication occurring the year after the field day (e.g., the third live event in 2021 had a book published in 2022 [51]). The total number of AgriSciences program participants have been 3649 + 2124 + 538 = 6311 since 2016. The most participants attended presentations (57.8%), followed by field days (33.7%) and congresses (8.5%).

3.5. Agricultural Professionals’ Training

Agricultural professionals’ responses to questions on their baseline training as well as time availability for training are summarized in

Table 8. Professionals’ participation and availability for training in middle-north region, Mato Grosso state, Brazil in 2019.

Professional Participation and Preferences	n	%
Participation in training and reading
Trainings attended annually (n = 88):
None	3	3.4
1–3 training(s) per year	33	37.5
4–5 trainings per year	16	18.2
>5 trainings per year	36	40.9
Technical articles read (n = 89):
Daily	23	25.9
Weekly	31	34.8
Monthly	10	11.2
Occasionally	24	27.0
Do not read technical articles	1	1.1
Books read annually (n = 89):
None	29	32.6
1–2 books	42	47.2
3–5 books	11	12.3
>5 books	7	7.9
Preferences for training:
Ideal length for agronomic course (n = 89):
1–4 h	32	36.0
8 h	21	23.6
>8 h	26	29.2
Do not know	10	11.2
Hours available for online training (n = 89):
Up to 1 h	46	51.7
1–3 h	26	29.2
A half day	6	6.7
Not interested in participating in online program	11	12.4
	0	0.2

. The number of trainings that agricultural professionals attend per year was bi-modal. There were 40.9% of respondents that attend 0 to 3 trainings annually, while 40.9% attend 5 or more. Most professionals also read technical articles and books. Only 1.1% of respondents do not read technical articles, while 98.9% read such articles at least occasionally. Almost half (47.2%) of professionals that responded read 1 to 2 books per year (Table 8).

The duration (number of hours) that professionals considered ideal for technical and online courses was also bi-modal. While over one-third of professionals (36%) considered one to four hours as the ideal time, 23.6% favored 8 h and 29.2% of respondents preferred trainings more than 8 h long. A minority (11.2%) did not know. Most professionals (51.7%) reported having only up to one hour per day available for online trainings. Others reported having one to three hours available (29.2%) or a half day (6.7%). The remaining respondents (12.4%) reported having no interest in online trainings (Table 8).

Agricultural professionals preferred trainings to be scheduled during times of the day, day(s), and month(s) that they were less busy, presumably when they were not consulting with producers. Saturday mornings were the most preferred, followed by weekday evenings compared to mornings and afternoons (Figure 4a). July and August are the most preferred months (Figure 4b) after second-crop maize (safrinha) has already been harvested or prior to cotton harvest. These two months correspond to the commodity cropping off-season in Mato Grosso during the middle of the dry season.

4. Discussion

4.1. Improved Participation for Sustainable Agricultural Systems

According to prior research, the Brazilian challenge is to maintain the capacity to produce and use soybeans profitably and sustainably [52]. Greater sustainability here involves crop rotation diversification and/or integrating commodity crops like soybeans and maize with perennial pasture for livestock grazing [53,54] or using soybeans during the pasture finishing of beef cattle in the region [55]. Integrated crop–livestock (ICL) systems have had low participation due to the management complexity of adding another enterprise or due to the coordination needed between specialized crop and livestock producers [56,57]. In addition to encouraging greater adoption of ICL, addressing social factors can also more broadly impact farm persistence and growth [58].

Access to rural credit in the Brazilian state of São Paulo increased the probability of adopting the ICL-Forest System (Sistema Integrado Lavoura-Pecuária-Floresta—ILPF) [59]. In our study, 81.7% of producers depend on financing for rural credit as a source for investment (Table 1), with 30.1% of surveyed producers using ICL systems (Table 3). Our ICL percentage was less than a prior assessment of integrated systems in Mato Grosso state where 89% of integrated systems involved just crops and livestock (rather than agro-forestry) and such ICL systems involved roughly 40.5% of 134 total producers (61 integrated + 73 non-integrated) surveyed [60]. Integrated systems are beneficial, diversifying rural revenues, recovering degraded pastures, improving physical, chemical, and biological attributes of soil, and reducing greenhouse gas emissions [61].

Increased participation of private companies in the development of agricultural markets in the 2010s was a major driver of production cost increases, which interfered with the direction of technologies and producers’ decision-making power [62]. Our results suggest that while most rural producers use strategies that may have more limited sustainability, many have adopted sustainable practices such as cover cropping. Sixty-nine percent of producers we surveyed used no-till which relies on glyphosate (i.e., Roundup^®) [63]. Glyphosate use in Brazil for soybeans has been shown to reduce erosion but increase herbicide resistance in weeds [64]. The prevalence of producers using Crotalaria juncea as a cover crop (46.3%) was similar to those using biological nitrogen fixation (47.6%). More complex and expensive strategies such as integrated crop–livestock systems and recovery of degraded pasture lands were used by only 17.9% and 7.1% of producers, respectively.

4.2. Increasing Participation in Public Sources of Agricultural Training

Our study identified factors influencing the use of public (versus private) sources of agricultural training by agricultural producers and professionals in the middle-north region of Mato Grosso state, Brazil. The entities that most contributed advisory support and capability toward developing programs were private agricultural companies and business support organizations (e.g., syndicates, associations). Public entities were less utilized, such as universities like Universidade Federal de Mato Grosso (UFMT), as well as state (e.g., EMPAER-MT) and federal (e.g., Embrapa) institutions. Brazilian agricultural commodity production lends itself to more dependence on the support industries for Brazilian agribusiness [65].

Our results suggest that UFMT and EMPAER-MT should continue to prioritize professional development activities for producers in rural areas (Table 3), such as on-farm field days [66]. Producers are more likely to be from smaller towns (30.8%) outside of the four major cities in the region compared to professionals (12.4%) and this difference is significant (p = 0.005, Table 3). In addition, AgriSciences also needs to hold on-farm events closer to Sinop where area producers are more likely not to use public training sources (p = 0.006, Table 6). Similarly, public training outreach to professionals from Sorriso needs to improve, since this group has lower participation with such extension (p = 0.0144, Table 6).

Well-structured technical assistance and rural extension can enhance field activities and improve rural development [67]. Publicly and privately funded agencies working in technology diffusion tend to become mediating or border organizations, assuming hybrid configurations such as multidisciplinary participatory platforms rich in partnerships, building innovations [68]. When training rural extension professionals, it is important to have continuity in conceptual discussion, systematization of innovative educational practices, and research on education, training, and results [69].

Hybrid learning platforms are also important, such as online videos and audio lessons, although such media are not widely deployed due to lack of internet connectivity in rural areas in Brazil [70]. Improved development of social media to enhance agricultural innovation and rural development in Canada has been limited by mobile telephone quality, having compatible technological equipment, and specialized technical assistance [71]. Keeping up with increasingly fast technological advances can ensure that rural producers and professionals are able to access updated information.

Both agricultural producers and professionals in the middle-north macro-region of Mato Grosso are more receptive to practical, in-service training while still being receptive to online training such as webinars, videos, etc. Professionals indicated the off-season period as the best time to improve their education and competencies. Therefore, the characterization provided in this study can be an excellent support to institutions in Mato Grosso state, Brazil and beyond that provide technical training, development, and assistance. Our results presented here can provide insight on how similar institutions can improve their training programs to both agricultural producers and professionals.

5. Conclusions

Identifying how to best meet the agricultural training needs of both agricultural producers and agricultural professionals from public extension sources is critical to improving the economic and environmental sustainability of commodity cropping systems. We surveyed 94 such producers and 89 agricultural professionals in the middle-north region of Mato Grosso state in Brazil’s midwest, which specializes in the production of soybean, maize, and cotton. Most producers were male, married, and varied in age, with 59.1% using a soybean–maize annual rotation. Relative to producers, agricultural professionals were more educated, with 52.8% having a college degree compared to only 22.8% for producers. Agricultural producers were more likely to use public versus private sources of training if they received less training from chemical companies and retailers, were from Sinop, female, unmarried, and if they managed less farm area. Agricultural professionals were more likely to use public agricultural training sources if they received less training from private chemical companies, were not from Sorriso, read more technical articles, and if they consulted producers on managing more crops on less farm area.

Our research is a start for better understanding the agricultural training needs of farmers and farm consultants in our region. However, our analysis could be repeated in the future to allow for pre–post testing of improved extension outreach over time, since we have contact information for all survey participants. Other regions in Mato Grosso (MT) state as well as Brazil can conduct similar surveys of agricultural producers and professionals in the future to compare to our results. Future public research and extension activities (e.g., field days on Saturday mornings and presentations on weekday evenings) in the middle-north region of MT need to continue to support producers and professionals involved in more diversified cropping systems. However, more targeted outreach to more specialized producers is needed, ideally on-farm in more rural areas. Future work can improve our understanding of how to better balance private sector training with agricultural extension outreach from the public sector (e.g., universities, state, and federal agencies) to encourage greater sustainability in commodity cropping systems in Brazil and elsewhere.