Research

INTRODUCTION

The urgent need to restore Earth’s natural environment (Díaz et al. 2019) coincides with the accelerating use of natural resources. The total annual global consumption of natural resources is expected to more than double from 2017 levels by 2060 because of growing human population and rising affluence (FAO 2022). Meeting humanity’s need for natural resources while concurrently restoring ecosystems requires a major transition toward more sustainable natural resource production. To be sustainable, natural resource management should not only protect non-human nature but also support human well-being and cultivate economic opportunities (Thiele 2016). The United Nations 2030 Agenda for Sustainable Development states that all sectors, including natural resource production, must consider the three dimensions of sustainability, i.e., economic, social, and environmental: “If the soil was bad, or if water was not managed well, then a farm might have been considered unsustainable. (...) If a farm is not economically sound or not resilient to external shocks, or if the well-being of those working on a farm is not considered, then a farm cannot be sustainable.” (SDG Indicator 2.4.1; FAO 2023).

However, insufficient attention has been paid in scientific literature and environmental governance to the diverse impacts that the implementation of top-down environmental targets and shifts to more environmentally friendly practices may cause to the well-being and livelihoods of natural resource producers (Plagányi et al. 2013, Bennett et al. 2019, Woods et al. 2021, Meyfroidt et al. 2022, McDermott et al. 2023). For example, in the past 30 years, millions of jobs in food production have been lost and the trend is predicted to continue (Brondizio et al. 2023). In many countries, the falling farmgate prices coinciding with increasing living expenses and input prices (the “cost-price squeeze”) has led to more concentrated natural resource production in the hands of fewer producers (Short et al. 2021, Stats NZ Tatauranga Aotearoa 2021, Schuch et al. 2022). Another example is provided by those target-based environmental policies that have contributed to a loss of local people’s access to natural resources (McDermott et al. 2023). Natural resource producers’ decreased well-being or unintended, unsupported, and/or involuntary transition to other livelihoods conflicts with the goal of sustainability (including the principle of equity) and may undermine the long-term success of ecosystem restoration (Bennett et al. 2019, Yletyinen et al. 2022). Thus, achieving more sustainable natural resource production requires also estimating property-level outcomes from environmental, economic, and social well-being dimensions and understanding how these three “pillars” of sustainability can be enhanced at the property-level.

A significant challenge in achieving and maintaining balance in environmental, social, and economic welfare in natural resource production is the dynamic nature of sustainability. Because the world is ever-changing, sustainability requires constant learning and adaptation (Thiele 2016). Hence, understanding resilience is vital to the progress toward sustainability (Marchese et al. 2018). It describes the capacity of natural resource production to adapt and persist in the face of change or make intentional changes that guide natural resource production into a more sustainable pathway when needed (Folke 2016, Marchese et al. 2018, Walker 2020). Critically, there are limitations to how many changes can take place at a property before the entire property or wider natural resource production system starts to change (Scheffer et al. 2012).

In this study, we shift the emphasis in studying natural resource production from the usual national- or international-level production and environmental targets (e.g., Tittensor et al. 2014, Díaz et al. 2019, Prime Minister’s Office 2020, Hinrich Foundation 2022) to property-level sustainability and resilience. Specifically, we investigate the ability of natural resource producers to achieve positive environmental, economic, and well-being outcomes at the property level, and identify factors that may underlie their resilience to do so. Natural resource production properties (e.g., farms, forestry properties) are the key site of action for sustainability and resilience in natural resource management (Darnhofer et al. 2010, Short et al. 2021). It is ultimately the property owner or manager who navigates the shifts in societal and environmental conditions, based on his/her perception of the potential, risks, and limits of each situation and the availability of resilience-enhancing resources (Darnhofer et al. 2010). Moreover, the impacts of environmental changes, as well as many new sustainability-seeking policies, directly impact natural resource production properties (FAO 2021, 2022, Blattert et al. 2023).

Resilience of a property, in turn, is an emergent trait and a process (Folke 2016). Properties for natural resource production are complex adaptive social-ecological systems in which the social and economic dimensions are embedded in the natural environment (Yletyinen et al. 2019). A property’s resilience (and eventually property-level sustainability) emerges from the complex interplay between intertwined environmental, social, and economic factors that enable the property to adapt to change (Folke et al. 2016, Yletyinen et al. 2019; Fig. 1), such as help from neighbors or material well-being. The Food and Agriculture Organization of the United Nations, for instance, considers adaptive capacity, assets, social networks, and access to basic services as determinants of the resilience of agricultural households (d’Errico et al. 2021). Furthermore, people whose livelihoods depend on natural resources may demonstrate a high capacity to cope with changes in economic conditions and maintain production (e.g., through further intensification of production practices), but potentially at the expense of environmental or social well-being, or vice versa. Natural resource systems are characterized by strong environment–economy–well-being interdependencies (Yletyinen et al. 2019).

Because of such complex interdependencies and trade-offs, an inadequate understanding of the resilience and sustainability of natural resource producers and their livelihoods at higher levels of natural resource management and decision making may underlie unintended negative social outcomes in the natural resource management (Plagányi et al. 2013, Short et al. 2021, Woods et al. 2021, McDermott et al. 2023). Framing the property holistically as a social-ecological system and thereby estimating which property-level factors may underlie positive environmental, social, and economic property-level outcomes can therefore open new perspectives on building resilient and sustainable natural resource production (Darnhofer et al. 2010). Furthermore, because resilience and sustainability assessments are typically built on external evaluations aiming for objective assessment of resilience, they may fail to capture the perceptions of individuals whom these assessments ultimately concern (Jones 2019). Yet, understanding the emergent nature of resilience and sustainability requires keen attention on the perceptions, judgments, and capabilities of individuals whose actions constitute the overall resilience and sustainability of social-ecological systems (e.g., Yletyinen et al. 2021a). Finally, relying on national-level measures for sustainability and resilience poses a significant risk of failing to notice in time property-level performance that may erode future resilience (Sundstrom et al. 2022).

An intriguing case for studying property-level outcomes in the primary industry is Aotearoa New Zealand (NZ), where the primary sector is a significant driver of the economy, rural well-being, and land use change (Cradock-Henry et al. 2019). Approximately half of NZ’s total land area is used for agriculture and forestry, and the food and fiber sector employs 13% of NZ’s total workforce (Ministry for the Environment & Stats NZ 2021, Ministry for Primary Industries 2022a). Tree plantations supply all of NZ’s industrial wood (Forest Owners Association 2022). In the 1980s, NZ’s primary production shifted from the goal of maximizing production with practices such as high use of fertilizers, to the goal of running a profitable business with succession opportunities (Knook et al. 2023). Subsidies were removed from agriculture in the 1980s (Saunders 2019). At present, NZ’s primary industry is increasingly focused on building resilient natural resource systems of production with more multifaceted goals, including environmental, financial, and well-being considerations (Knook et al. 2023, Ministry for Primary Industries 2020, 2022b). Since 2002, both the land area used for primary production and the number and size of farms have decreased (Ministry for the Environment & Stats NZ 2021).

NZ farmers, growers, and foresters have faced diverse environmental and societal changes and shocks, including severe earthquakes and some negative public perceptions of entire primary production sectors, e.g., the “dirty dairying” public campaign highlighted the negative environmental impacts of dairy farming (Ministry for Primary Industries 2017, Tall and Campbell 2018). Still, despite the recent rising input costs for natural resource producers, adverse weather events, and a difficult labor market during the COVID-19 pandemic, NZ’s agriculture and forestry sectors have continued to produce food and fiber for domestic and international markets (Ministry for Primary Industries 2022a). In 2021–2022, NZ’s food and fiber sectors even broke a new export revenue record, partly due to sharp increases in prices (Ministry for Primary Industries 2022a). Such an overall level of export performance during turbulent times has been suggested to be a demonstration of NZ’s primary industries’ resilience (Ministry for Primary Industries 2022a). Moreover, in 2022, NZ reached the top position of the Hinrich-IMD Sustainable Trade Index, which measures the readiness and capacity of 30 major trading economies to sustainable trade with 70 indicators that cover the three pillars of sustainability (Hinrich Foundation 2022). Yet, questions have been raised for years about the sustainability and long-term resilience of NZ’s natural resource production because of, for example, negative environmental impacts of land use intensification, farmers’ occupational stress, and limited rights of Indigenous Peoples (Brockerhoff et al. 2001, Ewers et al. 2006, Firth et al. 2007, Fitzharris 2007, Mackay 2008, Moller et al. 2008, Kenny 2011, Bataille et al. 2021, Ministry for the Environment & Stats NZ 2021, 2023, Etherington et al. 2022, Joy 2022, McClone et al. 2022, Renwick et al. 2022). The public has also expressed increasing concern for increasing foreign ownership of NZ primary production, which the public associates with the intensification of land use and negative impacts on the environment (Ministry for Primary Industries 2017).

In this study, we utilize a large survey dataset to investigate the property-level outcomes in NZ agriculture and forestry, including several natural resource production sectors, and identify factors that may underlie property-level positive environmental, financial, and social outcomes. We first ask how NZ farmers, foresters, and growers (i.e., producers who grow kiwifruit, wine grapes, flowers, seed crops, etc.) conceive of their environmental performance, financial situation, and personal well-being (the three pillars of sustainability). In the context of this study, we define positive property-level outcomes as strong or very strong environmental performance, a good or very good financial situation, and high individual well-being to include the three pillars of sustainability (e.g., Beachy 2010). The focus on good or very good outcomes is based on the notion that well-performing producers are most likely to remain on a desirable development pathway (such as sustainable natural resource production) despite evolving regulatory pressures, social expectations, and global environmental change, whether due to ecosystem resilience built by pro-environmental practices (Kremen and Merenlender 2018) or availability of economic resources that increase response capacity during crises (Walker 2019a, 2019b).

We then explore which factors may explain the ability of some NZ farmers, growers, and foresters to adapt to societal and ecological change and achieve positive outcomes. Prior research has shown that certain characteristics of natural resource producers and their operations can explain specific property-level outcomes such as resilience, although context-dependent variation in the significance of such characteristics is common (Knowler and Bradshaw 2007, Biggs et al. 2015, Kuhmonen 2020, Cradock-Henry 2021). Because all NZ farmers, foresters, and growers are embedded in the same national and global governance structures and systems of production and consumption, the ability of some to persist and thrive in primary production mainly emerges from individual or property-level traits and resources that enable adaptation and moving forward (Walker 2019a, Kuhmonen 2020, Cradock-Henry 2021). Although we here focus on individual and property-level factors, we recognize that property-level resilience emerges from many types of factors and processes within the wider social-ecological systems in which the resource producers are embedded (Gunderson and Holling 2004, Walker 2020). To answer this research question, we undertake regression analyses with a rich variety of individual- and property-level independent variables (Table 1) to identify factors associated with strong property-level environmental performance, a good financial situation, and individual well-being, one outcome at a time. The quality of each outcome is evaluated by the properties’ decision makers who are responsible for most long-term planning (Stahlmann-Brown 2021).

Finally, we focus only on those NZ farmers, foresters, and growers who have achieved positive outcomes in all three aspects, i.e., they perceive their environmental performance, financial situation, and well-being as being good or very good. Resilience research has shown that people whose livelihoods depend directly on natural resource production are better able to adapt to changing conditions when they are both environmentally sustainable and profitable; prioritizing economic, environmental, or well-being aspects to the exclusion of the others erodes property-level resilience (Plagányi et al. 2013, Darnhofer et al. 2016, Yletyinen et al. 2019). Detecting significant predictors that differ from the outcome-specific regression models may signal factors that explain the ability of these “triple bottom line” farmers, foresters, and growers to successfully mitigate potential trade-offs between environmental, financial, and well-being outcomes (Plagányi et al. 2013, Deng et al. 2016).

The agricultural and forestry sectors are immensely diverse and different sectors and properties will pursue different paths to remain in operation and meet the sustainability goals (Beachy 2010). This study strives to untangle the complex nature of sustainable and resilient resource production by exploring simultaneously environmental, financial, and well-being outcomes across multiple land-based natural resource sectors, and by investigating the possibility that different factors may underlie these three outcomes. In so doing, the study investigates the general resilience of NZ natural resource production properties from the perspective of the property owner/manager’s ability to navigate change and achieve positive outcome in his or her social-ecological setting. Identifying factors that support the natural resource producers to cope with change and gain positive outcomes contributes to maintaining diversity in resource production strategies and knowledge and, in so doing, to building resilient and sustainable natural resource systems (Plagányi et al. 2013, Biggs et al. 2015, Short et al. 2021, McDermott et al. 2023).

METHODS

Survey data

The Survey of Rural Decision Makers is a national-scale, repeated cross-section online survey of the primary sector conducted biennially since 2013. The questionnaire covers topics ranging from ownership structure, property classification, land use and land-use change, management regimes, future expectations, forestry and greenhouse gas emissions, to personal values, expectations regarding future climate, trusted sources of information, and demographics. Critically for our purposes, the 2021 wave of the survey also included modules covering perceived environmental performance, financial performance, and well-being (Stahlmann-Brown 2021).

More than 6700 farmers, foresters, growers, and “lifestyle block owners” (broadly speaking, people who do not depend on their rural properties for their livelihoods) completed the 2021 wave of the survey. We focus on the respondents who identify as being commercial operators. The survey data on commercial operators covers over 5% of the approximately 50,000 commercial farms, forests, and growing operations in NZ. The data is broadly reflective of primary industry in NZ, although dairy and sheep and beef farmers are intentionally oversampled. The data also reflects the demographic composition of NZ’s primary sector (Stahlmann-Brown 2021).

The 2021 questionnaire included nearly 600 potential data points for each respondent, although branching and question randomization meant that no respondent saw every question. In total 2496 survey respondents from commercial farms, forests, and growing operations answered specific questions about perceived environmental performance, financial situation, and personal well-being. Thus, we used the sample of 2496 respondents to answer the first research question. A subset of the 2496 survey respondents’ data was then used in the regression analysis because it was based on a much larger number of survey questions. Regression results reported below are for the 1672 commercial respondents’ subset who were presented with and who responded to all of the questions of interest.

The study design was reviewed for social ethics at Manaaki Whenua - Landcare Research under the guidelines of the Code of Ethics developed by the New Zealand Association of Social Science Researchers. This Code of Ethics emphasizes informed consent, freedom from coercion to participate, individual privacy, confidentiality, and sensitivity to participants’ circumstances. The survey included a statement of informed consent. It specified that participation is optional, that the respondents can stop answering the survey at any time, and that all responses are treated confidentially.

Regression analysis

We used binary logistic regression to identify individual- and property-level factors that correlate with the ability of NZ farmers, foresters, and growers to achieve strong environmental performance, a good financial situation, or/and high well-being. The independent variables included a rich variety of factors describing property-level agroecosystem features, environmental management and financial situation, land-owner/manager’s personal traits and social networks (Table 1, Appendix 1 Table S1). The probability of the outcome was predicted given known values of explanatory variables, which may be continuous or categorical. Specifically, we estimated:

(1)

in which Y is the dependent variable, b₀ is the intercept of Y, and b_n is the regression coefficient of the corresponding independent variable X. We used the forced entry method, in which all explanatory variables are included in the model simultaneously. Odds ratios, which measure the ratio of the odds that a farmer, forester, or grower belongs to the outcome category given the presence of an explanatory variable compared to the odds of it occurring in the absence of the explanatory variable, are reported. Odd ratios greater than 1 indicate that as the explanatory variable increases, the odds of the outcome occurring increase, and vice versa.

Dependent variables

Four regression analyses were undertaken. The first three used survey respondents’ perceptions of their properties’ environmental performance, their financial situation, and their well-being as dependent variables. In the fourth model, the dependent variable indicated respondents’ perception of having achieved all three desirable outcomes, i.e., strong environmental performance, financial situation, and well-being (hereafter, the “triple outcome”). Although the connection between producers’ subjective perceptions of their performance and the external, indicator-based measurements of performance is complicated (Kuhmonen 2020), for the purpose of our study, perceptions on outcomes provided holistic evidence on the performance of the property from the farmer, forester, or grower’s perspective. Importantly, they reflected the resource producer’s understanding on the resilience of the property (Berkes and Ross 2013, Kuhmonen 2020).

The binary dependent variables are defined as follows. First, to evaluate environmental performance, we analyzed response to the statement “The environmental performance of my operation over the last 12 months was ...” Specifically, we categorized respondents who replied “strong” or “extremely strong” (as opposed to “extremely weak,” “weak,” “slightly weak,” “neither weak nor strong,” “slightly strong”) as practicing behaviors and production techniques that maintain or restore ecosystem health. We acknowledge that the measure assumes a common reporting function, i.e., we assume that two different respondents with identical performance would complete the statement identically. However, self-reported measures of environmental performance have been shown to correlate with empirical measures of environmental performance (Pérez Urdiales et al. 2016), suggesting that environmental performance is well-understood among farmers, growers, and foresters. Moreover, NZ farmers, growers, and foresters are subject to strenuous environmental regulation that provides them direct evidence of their performance. For example, the NZ government has started to require freshwater farm plans for all properties with more than five hectares in horticultural use and more than 20 ha in arable, pastoral, or combined use; these plans are subject to both certification and audit against data collected by the regulator. As such, we contend that environmental perceptions are based on the producer’s expert knowledge and experience-based understanding of environmental processes at the property, and thus constitute an integral part of the capacities contributing to the resilience of the production system (Kuhmonen 2020).

A property’s overall financial situation indicates the ability of the producer to achieve a good livelihood from natural resource production. The survey respondents had answered the question “How do you assess the overall financial situation of the farm, forest, or growing operation?” with answer options “very good,” “good,” “neither good nor bad,” “bad,” “very bad,” “unsure,” or “prefer not to answer.” We identified those with answer options “good” or “very good” as survey respondents with the desirable financial outcome.

Well-being frameworks are increasingly used in natural resource resilience and sustainability research (Chaigneau et al. 2022, Chaigneau and Schill 2022) and high stress levels have become a significant concern in many rural communities (Firth et al. 2007, Kallioniemi et al. 2016, Naik 2017). For estimating survey respondents’ level of well-being, we used the World Health Organization’s Five Well-being Index (WHO-5). Brown et al. (2021) recommend subjective well-being measures as part of a sustainability indicators framework. The WHO-5 is the most common measure of current subjective mental well-being used worldwide (Topp et al. 2015). It is based on five survey questions in which respondents estimate how often, over the past two weeks, she/he (1) has felt cheerful and in good spirits, (2) has felt calm and relaxed, (3) has felt active and vigorous, (4) woke up feeling fresh and rested, and (5) daily life has been filled with things that interest me, each with answer options: “all of the time,” “most of the time,” “more than half the time,” “less than half the time,” “some of the time,” or “at no time” (Mental Health Services [date unknown]). The maximum score for each question that the responder can get is five, from the answer option “all of the time,” whereas the consequent answer options give the scores of four to zero. Thus, the maximum WHO-5 score is 25. A cumulative score below 13 is commonly identified as poor well-being, whereas a result of 25 indicates the best possible well-being and zero the worst imaginable well-being (Topp et al. 2015). For example, Stats NZ Tarauranga Aotearoa, which is NZ’s official data agency, uses the threshold of 13 for poor well-being. There is no agreed upon WHO-5 threshold indicating sufficiently high well-being. In this study, we interpreted the respondents with WHO-5 scores of 17 or over to have high well-being. In line with the two other dependent variables, we strived to capture good well-being and not slightly good or neither good nor bad well-being, which values closer to 13 might have indicated. The WHO-5 is commonly translated to percentage scale by multiplying the raw score four, in which score zero represents the worst imaginable well-being and 100% the best imaginable well-being (Topp et al. 2015). The WHO-5 score of 17 translates to circa 70%, indicating above average well-being. Our results on factors associated with well-being are largely unchanged when we vary the cut-off between WHO-5 scores 15 and 20.

While the WHO-5 is subject to common reporting function, it has been validated in several dozen countries in Africa, Asia, Europe, The Americas, the Middle East, and Oceania (Topp et al. 2015). Moreover, measured well-being is consistent and intuitive: for example, victims of torture report low well-being, on average; people in high-income countries report high well-being, on average; and people living with chronic disease report lower well-being than the general population (Topp et al. 2015). Technically, the triple outcome producers are those who had responded “good / strong” or “very good / strong” to both the environmental performance and financial situation questions as described above, and additionally gained a WHO-5 score of 17 or higher.

Regression analysis is normally predicted on the basis that the dependent variable is cardinal rather than ordinal. However, our data are ordered and categorical. Some researchers (Ferrer-i-Carbonell and Frijters 2004) have advocated the use of ordered logit estimation for ordered categorical data, but Bond and Laing (2019) show that even ordered logit estimation imposes assumptions on the distribution. Thus, we follow the approach advocated by Bloem and Oswald (2022), which is to split the sample into “high” and “low” categories, (e.g., “good or very good financial situation” and “other financial situation”) on the dependent variable.

Each of our binary logistic regressions imposes no distributional assumption on the data other than ordering. Our “triple outcome” regression is also binary logistic, measuring whether the respondent concurrently achieves the “high” outcome in financial performance, environmental performance, and well-being.

Independent variables

From the perspective of resilience-based natural resource management, a property for natural resource production is a unit consisting of its owner or manager with personal mental models, preferences, abilities, etc. that provide for adaptation social and cultural capital, and a biogeophysical component including land, crop, buildings, and other resource ecosystem properties that provide natural and economic capital (Darnhofer et al. 2016, van der Lee et al. 2022). Hence, similar to Kuhmonen’s recent study (2020), we selected a wide variety of explanatory variables that reflect a producer’s individual agency (e.g., farm experience) and the structure of operation (e.g., area of the property; right-hand side column in Table 1, for detailed definitions see Appendix 1 Table S1). We include explanatory variables that describe individual traits, economic status of the property, the agroecosystem setting, including landscape features, and the property owner’s/manager’s social interactions in terms of who the farmers, growers, and foresters consider the most trusted sources of advice and information. Although the selection of variables was data-driven, it was guided by research on resilience determinants, as summarized in Table 1.

RESULTS

Positive property-level outcomes among NZ farmers, foresters, and growers

The survey data suggests that despite turbulent times, most NZ farmers, foresters, and growers estimated their financial situation and/or environmental performance as good or very good in 2021 (Figs. 2 and 3). Specifically, over half of the 2496 respondents who had answered questions about the outcomes estimated their environmental performance as strong or very strong (n = 1362, 54%). Note that the number of responders is larger here than in the regression analyses because the regression analysis was limited to the responders who had been presented and had answered all the questions included in the models. Similarly, 60% of the respondents estimated their financial situation to be good or very good (n = 1487). Half of the respondents (n = 1292, 52%) reported a WHO-5 score of 17 or over, indicating good well-being (Fig. 4). Further, our results suggest that approximately one-fifth of the NZ farmers, foresters, and growers in our dataset of 2496 respondents had achieved the combination of strong environmental performance, good financial situation, and good well-being (523 respondents, 21%).

Descriptive statistics (Table 2) show that the triple outcome group is, on average, older and more experienced, more educated, and more profitable than other respondents. In addition, their properties are larger, on average.

Factors associated with positive outcomes in NZ agriculture and forestry

We conducted four binary logistic regression analyses to investigate which individual- and property-level characteristics correlate with (i) strong environmental performance, (ii) good financial situation, (iii) high well-being, and (iv) all three outcomes combined (Table 3, Figs. 5–8). The pseudo-R-squared statistics indicate that all four models fit the data better than those containing the constant only (Appendix 1 Tables S2–S5). Figures 5–8 present the odds ratios from the perspective of each outcome, whereas the following section reports the results from the perspective of different factors.

Individual traits and views

Individual traits and views emerged as significant independent variables in all four models. Our results (Table 3) show that the personal view on the strength of environmental performance of own property in the future was the only individual-level explanatory variable that significantly correlated with all three outcomes as well as the triple outcome. The correlation was positive for all the outcomes. The similar personal view “it is important to me that the public see farmers, foresters, and growers doing their part for the environment,” on the other hand, was not a significant explanatory variable to any of the desirable outcomes. Strikingly, strong environmental performance also correlated significantly with another personal view: believing that climate change is not real was associated with strong environmental performance.

Feeling frequent emotional exhaustion correlated negatively with good financial status, well-being, and the triple outcome, but did not relate to environmental performance. The frequency of feeling physically exhausted was associated with lower odds of high well-being, but not the other outcomes. Further, strong environmental performance was positively correlated with experience. Age, level of education, willingness to experiment with new ideas, and the number of generations that the family has been involved in farming, forestry, or growing food did not significantly correlate with the dependent variables of our models.

Agroecosystem characteristics of the property

Agroecosystem characteristics, too, correlated with all outcomes and the triple outcome. Comparing the odds ratios for agroecosystem characteristics in the four regression models indicates that the area of the property predicted a higher likelihood of strong environmental performance and good financial status but lowered the likelihood of achieving high well-being. Having specific landscape features on the property such as native bush or wetlands was not significantly correlated with strong environmental performance. However, having land on a property that is not suitable for production (e.g., gullies) correlated negatively with a good financial situation and high well-being.

Of the five primary industries included in the models, forestry was associated with strong environmental performance. Sheep and beef, dairying, and horticulture were negatively correlated with well-being. Finally, the results indicate a relationship between the location of the property and good financial situation as well as the triple outcome, but not with high well-being or strong environmental performance (see point estimates for specific regions in Table 3).

Environmental management foci and implemented environmental management practices

Our models included four environmental management foci. Both “managing biosecurity” and “increasing native biodiversity” as environmental management foci correlated positively with strong environmental performance and the triple outcome. Among the implemented environmental management practices, we detected only one statistically significant explanatory variable, namely, reduction of soil erosion was associated with strong environmental performance.

Finances

Whether the farm, forest, or growing operation had been either profitable or break even (in contrast to being unprofitable) in the previous two years is positively correlated with having a good financial situation, high well-being, and the triple outcome. The models show a negative relationship between total debt and good financial status and the triple outcome. Total debt did not statistically correlate with high well-being. Neither profitability nor debt correlated significantly with strong environmental performance.

Social interactions

Finally, considering peers and peer support groups as the most helpful sources for advice is positively correlated with having high well-being. The other significant social interaction variables in our models correlated negatively with the good financial situation, strong environmental performance, and the triple outcome. Namely, considering social media as the most helpful source of advice correlated negatively with the triple outcome, and having accountants, banks, and insurers as the most helpful source for advice and information correlated negatively with good environmental performance (p < 0.01).

DISCUSSION

Our results suggest that despite turbulent times, most farmers, growers, and foresters have been able to achieve at least moderate property-level outcomes in NZ’s current resource regime. Over half of NZ’s land-based natural resource producers achieved environmental, social, or economic outcomes that they themselves considered good or very good. The finding on the majority of producers achieving moderate or better outcomes in at least one sustainability dimension is compelling, since outcome variability between individual natural resource properties may greatly vary (Greer et al. 2008, Cradock-Henry and Fountain 2019).

In so doing, our results indicate general property-level resilience in NZ as the capacity of farmers, growers, and foresters to persist and achieve good or very good outcomes. That said, many producers not able to achieve at least moderate outcomes may have already transitioned to other livelihoods; the total number of farms in NZ has decreased by nearly 30% from 2002 to 2019 (Stats NZ Tatauranga Aotearoa 2021, Ministry for the Environment & Stats NZ 2022). Nevertheless, our results for property-level resilience across primary industry sectors are in agreement with other studies that have reported rural NZ as resilient, including studies from the perspective of financial resilience of farms (Greig et al. 2019), resilience to shocks (Cradock-Henry et al. 2018), and multiple stressors on specific primary industry sector (Cradock-Henry and Fountain 2019). Still, to the authors’ knowledge, this is the first study to estimate NZ’s property-level sustainability (including all three pillars), as conceived by the farmers, growers, and foresters, as well as factors associated with resilience and positive outcomes across several land-based primary industry sectors.

A minority of NZ farmers, growers, and foresters reported bad or very bad outcomes in environmental and financial dimensions. Poor well-being was not more common for NZ farmers, growers, and foresters than for people living in NZ in general (Stats NZ Tatauranga Aotearoa 2022). NZ’s natural resource governance started to pay attention to human well-being mostly in the recent decade, e.g., the rural mental health organization Farmstrong NZ was initiated in 2015 (Knook et al. 2023). Thus, our results on less bad or very bad outcomes in environmental and financial than well-being outcomes may reflect NZ’s stronger focus in natural resource management on property-level production and environmental outcomes than the well-being of the producer.

All that said, we found that achieving good or very good property-level outcomes in all three dimensions was not common among NZ resource producers, which essentially would indicate the highest level of property-level sustainability. The variables related to the triple outcome were of a self-fulfilling nature in our models. However, our regression analyses for outcome-specific factors provide a plausible explanation for the relatively rare occurrence of these overall well-performing producers. The results show that the three positive outcomes correlate with different factors to the extent that an independent variable can have a positive correlation with one outcome and a negative one with another. For example, property size correlated positively with having a good financial situation but negatively with high well-being. Further, emotional exhaustion, having land unsuitable for production on property, and profitability correlated with having a good financial situation and high well-being (whether positively or negatively) but were not significantly associated with environmental performance. Certain environmental management foci and implemented environmental practices, on the other hand, correlated with environmental performance but not with the property’s financial situation or personal well-being. Yet another example is social groups identified as the most helpful sources of advice. None of the social groups included in our study correlated with all three outcomes. Altogether, our study indicates that achieving the triple outcome is difficult; there are a few common property-level drivers to the positive outcomes and some factors have conflicting effects. In other words, there is generally no “winning recipe” identifiable among the triple outcome farmers in our models.

The only factor that significantly and positively correlated with all the three positive outcomes was a plan or ambition to achieve strong environmental performance at own property in the future. The related view “it is important that the public sees farmers, foresters, and growers doing their part for the environment,” on the other hand, was not significantly associated with any of the outcomes. Thus, the goal for strong environmental performance may not be due to the social pressure or it may reflect an earlier finding that many resource producers consider the media, not themselves, to be the most impactful influencer of public opinions (Knook et al. 2022). The intriguing finding about the strong future environmental performance of own operation may mean that the NZ farmers, foresters, and growers who achieved strong or very strong environmental, financial, or well-being outcomes are generally optimistic. The result might suggest that these respondents were optimistic about their current environmental performance, financial situation, and well-being too. Hence, their survey responses on the outcomes of their operation as strong or very strong may be biased by their optimistic view on life. Another explanation can, however, be found in psychology’s view on resilience: optimism as a personal trait is one of the strongest determinants of human resilience, together with social connectivity and sense of humor (Walker 2019a). If the survey question on future environmental performance indeed captures optimism, it would be the key determinant in our models for NZ farmers, foresters, and growers’ ability to cope and achieve positive outcomes in natural resource production. In any case, NZ primary industry would benefit from including in the sustainability transition the “inner worlds” of people, i.e., emotions, thoughts, identities, and beliefs, e.g., by adopting terminology that is not only scientifically precise but also fosters positive emotions, and by encouraging practices that promote mental health (Ives et al. 2020).

All in all, our results emphasize the importance of measuring the performance of natural resource production simultaneously with environmental, economic, and social objectives (Plagányi et al. 2013). Our results on the difficulty of achieving the triple outcome demonstrates that strengthening ecosystem resilience through good environmental performance does not necessarily translate into greater social outcomes at property-level, or vice versa, or into sustainable natural resource production (Adger 2000, Walker 2019a, 2019b, Yletyinen et al. 2019, Woods et al. 2021). Therefore, as the goal of natural resource management shifts from a singular goal (e.g., profitability) to the multi-faceted sustainability and resilience, natural resource governance must increasingly consider environmental–economic–well-being interdependencies among the factors driving the three outcomes to successfully transition to more sustainable natural resource production. Further, trade-offs associated to sustainability in natural resource management have been detected in natural resource management (especially between nature conservation and economic utilization of the resource), among others in fisheries, forestry, and livestock farming (Plagányi et al. 2013, Castonguay et al. 2023, Mazziotta et al. 2023). The studies on conflicting outcomes often focus on their likely causes, such as resource management strategies, and a few studies have included stakeholders’ own perspectives to the conflicts (Klapwijk et al. 2014). Our results, on the other hand, encourage investigating solutions to conflicting outcomes by testing a vast array of factors associated with all three dimensions of sustainability, and including factors that characterize natural resource producers (e.g., personal views, on-farm experience, social connections). Such social factors can provide important leverage points for achieving sustainability outcomes (Yletyinen et al. 2021a). Moreover, the more holistic analyses may capture emergent phenomena, such as the effects of increasingly large properties on well-being of the property owners/managers, and show that neither nature nor economy alone should be the basis of sustainability policies (Berglund 2001). Resource producers typically already consider social-ecological interdependencies, for example, by associating profitability with better work-life balance, or environmental performance with ecosystem resilience and, thus, profitability (Knook et al. 2023). Thus, transitioning to a more sustainable and resilient natural resource production in NZ may benefit from emphasizing the role of natural resource producers as adaptive agents (Gosnell et al. 2020).

Although our study is based on a carefully designed, detailed, national dataset and well-established method, we acknowledge some caveats in the study. Using survey data both limited and provided novel opportunities for explanatory variable selection. For example, knowledge on the property-level management strategy would have enabled coupling the outcomes to the property-specific goals of management, which would have provided further evidence on achieving outcomes desired by the resource producer. Our results on the factors contributing to the triple outcome indicate that the model would have benefitted from independent variables that are known to contribute to mitigating trade-offs, such as external income sources (Mishra et al. 2012), or diversity of crops and harvesting strategies (Klapwijk et al. 2014). Further, while the financial situation is illustrated by the property’s bookkeeping and well-being was measured with the well-established WHO-5 index, self-reported environmental performance may be biased by the respondent’s knowledge of ecosystem health and need for environmental action. Nonetheless, as stated earlier, NZ farmers, growers, and foresters gain feedback for their environmental performance and in general, being a farmer, forester, or grower requires expertise in ecological processes. Moreover, perceptions provide important evidence about the way resource producers view the outcomes of their actions and can challenge preconceived ideas about their actions and what is important to them (Nicholas-Davies et al. 2020). Finally, it is important to bear in mind that studies like ours must be complemented with larger scale social-ecological systems research to gain a comprehensive understanding on what builds resilience in natural resource production and what kind of property-level outcomes would maintain resilient natural resource systems on regional and national scales (Gunderson and Holling 2004, Darnhofer et al. 2016).

Natural resource management is often characterized by multiple, sometimes conflicting ideas of what sustainability is (Meyfroidt et al. 2022), including the question of whether all three outcomes at properties should be good or just not “bad” for NZ to have sustainable natural resource production. Moreover, resilience is a multi-level concept and individual- or property-level resilience may not translate to regional or national resilience (Leite et al. 2019). Property-level resilience may even hinder transition to more sustainable natural resource production if the transition has not gained social license from NZ farmers, growers, and foresters, since their resilience entails ability of the property to adapt in order to not be changed (Ives et al. 2020, Walker 2020). Our study completes the picture of NZ’s rural resilience with property-level investigation for the purpose of studying how NZ natural resource producers are coping, but cross-scale resilience research is needed to understand how the resilience of NZ properties affects the ability of NZ’s primary industries to transition to more sustainable production in a fair manner. Gaining knowledge on factors underlying resilience and positive outcomes at property-level is vital especially when natural resource governance is using the broad concepts of resilience and sustainability to focus policy statements and strategies around them (e.g., NZ’s Fit for a Better World program [Ministry for Primary Industries 2020]), but lack articulation of the concepts that is clear and detailed enough to be translated into mechanisms that support natural resource production (Roche 2017, Young et al. 2018). Identification of factors that are associated with positive outcomes could contribute to development of specific resilience tools and, consequently, to knowing whether and how managing resilience has been achieved (Young et al. 2018).

Producing food and fiber requires constant adaptation. By utilizing a holistic approach and a pertinent survey data, this study provides new knowledge on the factors associated with positive property-level outcomes in NZ’s land-based natural resource production. In conclusion, our results indicate that achieving sustainability as good or very good environmental, economic, and well-being outcomes at property-level is a complex challenge, although not impossible. This study demonstrates that careful consideration for aligning the three sustainability objectives may contribute to decreasing unintended negative impacts of top-down environmental targets or shifts to more sustainable resource production (Plagányi et al. 2013, Woods et al. 2021, McDermott et al. 2023). Understanding and managing the resilience of natural resource producers to environmental and socioeconomic change will contribute to more equitable sustainability transformations (Short et al. 2021, Schuch et al. 2022, McDermott et al. 2023).

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