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More than 15% of global terrestrial area is under some form of protection and there is a growing impetus to increase this coverage to 30% by 2030. But not all protection is effective and the reasons some countries’ protected areas (PAs) are more effective than others’ are poorly understood. We evaluate the effectiveness of national PA networks established between 2000 and 2012 globally in avoiding forest loss, taking into account underlying deforestation threats using a combination of matching methods and cross-sectional regressions. We then assess which demographic, agricultural, economic, and governance factors are most strongly associated with national PA effectiveness using machine learning methods. We estimate that national PAs established between 2000 and 2012 reduced deforestation in those areas by 72%, avoiding 86 062 km 2 of forest loss. The effectiveness of national PAs varied by strictness of protection based on International Union for Conservation of Nature category. Strictly PAs reduced forest loss by 81% compared to what would have occurred without protection, while less strictly PAs reduced forest loss by 67%. Thus, the 26% of new PAs that were strictly protected contributed 39% of the total forest loss avoided within PAs between 2000 and 2012. If every country’s PAs were as effective as the country with the most effective PAs within the same region, they would have increased the area of deforestation avoided by 38%, saving a further 119 082 km 2 of forest. Part of the variation in PA effectiveness across countries is explained by the placement of PA in areas facing higher deforestation threat. Countries with lower agricultural activity, higher economic growth and better governance are most strongly associated with greater country-level PA effectiveness.

Background Agroforestry bridges the gap that often separates agriculture and forestry by building integrated systems to address both environmental and socio-economic objectives. Existing empirical research has suggested that agroforestry—the integration of trees with crops and/or livestock—can prevent environmental degradation, improve agricultural productivity, increase carbon sequestration, and support healthy soil and healthy ecosystems while providing stable incomes and other benefits to human welfare. However, the extent of the literature supporting or refuting these claims has not been well documented. This study addresses this research gap by collating and describing the evidence for the impacts of agroforestry on ecosystem services and human well-being in high-income countries and presents the characteristics and gaps in the literature. Methods We searched 5 primary databases and 24 organizational websites using a pre-defined search string designed to capture articles relating agroforestry practices and policy interventions to outcomes in high-income countries. Searches included peer-reviewed and grey literature published in the English language between January 1990 and June 2020. We screened the identified articles for inclusion or exclusion in two stages: title/abstract and full text. We extracted data from articles included at the full-text stage to form the map and associated database. For inclusion, the study in question must have assessed the impacts of the deliberate promotion and/or actual integration of woody perennials (trees, shrubs, etc.) with agricultural crops and/or animals. Results Our search returned 31,852 articles of which we included 585 primary articles, 6 ongoing primary articles, and 41 systematically conducted literature reviews. The articles spanned three decades and 31 countries. The most studied practices are on linear boundary plantings (hedgerows, shelterbelts, windbreaks, and riparian buffers) and silvopasture systems. The most studied outcome is regulation and maintenance of physical, chemical, and biological conditions as an ecosystem service, followed by agricultural yield and mediation of waste/toxics/other nuisances (nutrient runoff and carbon storage). Conclusions Results highlight key evidence gaps and areas where research has concentrated. Knowledge on the impacts of specific policy interventions to promote agroforestry remains scarce. The impacts of actual agroforestry practices are more well-studied, but the kinds of practices studied are limited, with most research focusing on two-component systems consisting of a simple tree configuration with one crop or livestock species, such as shelterbelts, windbreaks, and hedgerows, riparian buffers, and scattered trees on farms with crops and/or livestock. Regulating ecosystem services outcomes are by far the most studied, followed by agricultural productivity (an aspect of provisioning ecosystem services), while evidence on human well-being remains limited. We also found geographic biases, with little to no evidence for many countries. These biases suggest the strong need for further research to build the evidence base on agroforestry across high-income countries. The results can inform future research and policy decisions by making the evidence easily accessible and highlighting knowledge gaps as well as areas with enough evidence to conduct further systematic review.

Establishing legal protection for forest areas is the most common policy used to limit forest loss. This article evaluates the effectiveness of seven Indonesian forest protected areas introduced between 1999 and 2012. Specifically, we explore how the effectiveness of these parks varies over space. Protected areas have mixed success in preserving forest, and it is important for conservationists to understand where they work and where they do not. Observed differences in the estimated treatment effect of protection may be driven by several factors. Indonesia is particularly diverse, with the landscape, forest and forest threats varying greatly from region to region, and this diversity may drive differences in the effectiveness of protected areas in conserving forest. However, the observed variation may also be spurious and arise from differing degrees of bias in the estimated treatment effect over space. In this paper, we use a difference-in-differences approach comparing treated observations and matched controls to estimate the effect of each protected area. We then distinguish the true variation in protected area effectiveness from spurious variation driven by several sources of estimation bias. Based on our most flexible method that allows the data generating process to vary across space, we find that the national average effect of protection preserves an additional 1.1% of forest cover; however the effect of individual parks range from a decrease of 3.4% to an increase of 5.3% and the effect of most parks differ from the national average. Potential biases may affect estimates in two parks, but results consistently show Sebangau National Park is more effective while two parks are substantially less able to protect forest cover than the national average.

Fire hydrants are critical to urban fire response, giving emergency personnel access to water to protect communities and property. In coastal California’s Mediterranean climate, with hot, dry summers and occasional strong winds, understanding how hydrant networks, urban form, and response operations influence structure fire outcomes is essential. This study examines past structure fire incidents in urban areas of Santa Barbara County to (i) better understand where fires occur and (ii) identify drivers of loss and occurrence, with particular attention to how hydrant infrastructure relates to these outcomes. Spatial mapping is combined with statistical models of incident counts and monetary losses. Fires are primarily clustered around major activity centers. While hydrant availability is not associated with incident occurrence, population and housing density are primary predictors of where incidents occur. Loss severity increases with total suppression time and property area, and incidents beginning near dawn have substantially higher expected losses. After controlling for operational factors and property type, hydrant proximity and code-compliant access are not independently associated with loss magnitude, suggesting that damage in this setting is more sensitive to time to control than to differences in hydrant proximity. These findings support density-focused prevention efforts and call attention to reducing suppression time to limit damage.

Background Agroforestry, the intentional integration of trees or other woody perennials with crops or livestock in production systems, is being widely promoted as a conservation and development tool to help meet the 2030 UN Sustainable Development Goals. Donors, governments, and nongovernmental organizations have invested significant time and resources into developing and promoting agroforestry policies and programs in low‐ and middle‐income countries (LMICs) worldwide. While a large body of literature on the impacts of agroforestry practices in LMICs is available, the social‐ecological impacts of agroforestry interventions is less well‐studied. This knowledge gap on the effectiveness of agroforestry interventions constrains possibilities for evidence‐based policy and investment decisions to advance sustainable development objectives. Objectives The primary objective of this Campbell systematic review was to synthesize the available evidence on the impacts of agroforestry interventions in LMICs on agricultural productivity, ecosystem services, and human well‐being. The secondary objectives were to identify key pathways through which agroforestry interventions lead to various outcomes and how the interventions affect different sub‐groups of the population. Search Methods This review is based on a previously created evidence and gap map (EGM) of studies evaluating the impacts of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well‐being. We included published and unpublished literature in the English language covering the period between 2000 and October 20, 2017. We searched six academic databases and 19 organization websites to identify potentially relevant studies. The search was conducted for our EGM in mid‐2017, and we did not conduct an additional search for this systematic review. Selection Criteria We included randomized control trials (RCTs) and quasi‐experimental studies assessing the effect of an agroforestry intervention on at least one outcome measure of agricultural productivity, ecosystem services, or human well‐being for farmers and their farmland in LMICs. Agroforestry interventions include any program or policy designed to promote and support the adoption or maintenance of agroforestry practices, which include trees on farms, silvopasture, shade‐grown crops, and homegardens with trees, among others. Moreover, the studies needed to include a nonagroforestry comparator, such as conventional agriculture or forestry systems or a before‐after comparison. Data Collection and Analysis We used a standardized data extraction spreadsheet to extract details about each included study. We also used a standardized form to assess risk of bias for each of the included studies in this SR. Meta‐analysis techniques were used to combine and synthesize effect size estimates for the outcomes measures that had sufficient data. We used a random effects models for the meta‐analyses and use Hedge's g (difference in means divided by the pooled standard deviation) to report effect size estimates. The outcomes without enough evidence for meta‐analysis were discussed narratively. Main Results We identified 11 studies across nine countries, all of which used quasi‐experimental methods. Overall, the quality of the evidence base was assessed as being low. Studies were rated as having high or critical risk of bias if they failed to convincingly address more than one of the main potential sources of bias, namely selection bias, group equivalence, and spillover effects. Given the low number of studies and the high risk of bias of the evidence base, the results of this SR are limited and should be considered a baseline for future work. The results of the meta‐analysis for impacts on yields indicated that agroforestry interventions overall may lead to a large, positive impact on yield (Hedge's g = 1.16 [−0.35, 2.67] (p = .13)), though there was high heterogeneity in the results (I2 = 98.99%, τ 2 = 2.94, Q(df = 4)  = 370.7). There were positive yield impacts for soil fertility replenishment practices, including incorporating trees in agricultural fields and improved fallow practices in fields where there are severe soil fertility issues. In other cases, incorporating trees into the production system reduced productivity and took land out of production for conservation benefits. These systems generally used an incentive provision scheme to economically offset the reductions in yields. The result of the meta‐analysis on income suggests that agroforestry interventions overall may lead to a small, positive impact on income (Hedge's g = 0.12 [−0.06, 0.30] (p = .20)), with moderately high heterogeneity in the results (I2 = 75.29%, τ 2 = 0.04, Q(df = 6) = 19.16). In cases where improvement yields were reported, there were generally attendant improvements in income. In the cases where payments were provided to offset the potential loss in yields, incomes also generally improved, though there were mixed results for the certification programs and the tenure security permitting scheme. One program, which study authors suggested may have been poorly targeted, had negative yield impacts. There was not enough comparable evidence to quantitatively synthesize the impacts of agroforestry interventions on nutrition and food security outcomes, though the results indicted positive or neutral impacts on dietary diversity and food intake were likely. Surprisingly, there was little evidence on the impacts of agroforestry interventions on environmental outcomes, and there was no consistency of environmental indicator variables used. However, what has been studied indicates that the environmental benefits are being achieved to at least some extent, consistent with the broader literature on agroforestry practices. The evidence base was insufficient to evaluate the interaction between environmental and social impacts. Several studies explicitly considered variable impacts across different population sub‐groups, including differential impacts on small‐holders versus large‐holders, on woman‐headed households versus male‐headed households, and on richer groups versus poorer groups. Small‐holder farmers typically experienced the most positive effect sizes due to the agroforestry interventions. Women and poorer groups had mixed outcomes relative to men and richer households, highlighting the importance of considering these groups in intervention design. Authors' Conclusions There is limited evidence of the impacts of agroforestry interventions, restricting our ability to draw conclusions on the effect sizes of different intervention types. The existing evidence forms a baseline for future research and highlights the importance of considering equity and socio‐economic factors in determining suitable intervention design. Some key implications for practice and policy include investing in programs that include pilot programs, funding for project evaluation, and that address key equity issues, such as targeting to smallholders, women, poor, and marginalized groups. Funding should also be given to implementing RCTs and more rigorous quasi‐experimental impact evaluations of agroforestry interventions over longer time‐periods to collect robust evidence of the effectiveness of various schemes promoting agroforestry practices.

Background Agroforestry bridges the gap that often separates agriculture and forestry by building integrated systems that address both environmental and socio-economic objectives. Agroforestry can improve the resiliency of agricultural systems and mitigate the impacts of climate change. Existing research suggests that integrating trees on farms can prevent environmental degradation, improve agricultural productivity, increase carbon sequestration, generate cleaner water, and support healthy soil and healthy ecosystems while providing stable incomes and other benefits to human welfare. Although these claims are becoming more widely accepted as the body of agroforestry research increases, systematic understanding of the evidence supporting them remains lacking for high-income countries. This systematic map will address this research need by providing a tool for identifying and visualizing the existing evidence demonstrating the impacts of agroforestry practices and interventions on agricultural productivity, ecosystem services, and human well-being. The results will be useful for informing policy decisions and future research by making the evidence easily accessible and highlighting the gaps in knowledge as well as areas with enough evidence to conduct systematic reviews. Methods This systematic map will identify, collect, display, and describe available evidence on the impacts of agroforestry on agricultural productivity, ecosystem services, and human well-being in high-income countries. The search strategy will cover 5 primary databases and 24 organizational websites using a pre-defined search string designed to capture studies relating agroforestry practices and interventions to outcomes in high-income countries. The searches will all be conducted in English. We will screen the identified studies for inclusion or exclusion in stages, first on title and abstract and then on full-text. We will collect data from studies included at the full-text stage to form the map and associated database. For inclusion, the study in question must assess the impacts of the deliberate promotion and/or actual integration of woody perennials (trees, shrubs, palms, bamboos, etc.) on the same land management unit as agricultural crops and/or animals.

An important part of conservation practice is the empirical evaluation of program and policy impacts. Understanding why conservation programs succeed or fail is essential for designing cost‐effective initiatives and for improving the livelihoods of natural resource users. The evidence we seek can be generated with modern impact evaluation designs. Such designs measure causal effects of specific interventions by comparing outcomes with the interventions to outcomes in credible counterfactual scenarios. Good designs also identify the conditions under which the causal effect arises. Despite a critical need for empirical evidence, conservation science has been slow to adopt these impact evaluation designs. We identify reasons for the slow rate of adoption and provide suggestions for mainstreaming impact evaluation in nature conservation.

We assess the additional forest cover protected by 13 rural communities located in the southern state of Chiapas, Mexico, as a result of the economic incentives received through the country's national program of payments for biodiversity conservation. We use spatially explicit data at the intra-community level to define a credible counterfactual of conservation outcomes. We use covariate-matching specifications associated with spatially explicit variables and difference-in-difference estimators to determine the treatment effect. We estimate that the additional conservation represents between 12 and 14.7 percent of forest area enrolled in the program in comparison to control areas. Despite this high degree of additionality, we also observe lack of compliance in some plots participating in the PES program. This lack of compliance casts doubt on the ability of payments alone to guarantee long-term additionality in context of high deforestation rates, even with an augmented program budget or extension of participation to communities not yet enrolled.

Weather conditions can drastically alter the state of crops and rangelands and, in turn, impact the incomes and food security of individuals worldwide. Satellite-based remote sensing offers an effective way to monitor vegetation and climate variables on regional and global scales. The annual peak normalized difference vegetation index (NDVI), derived from satellite observations, is closely associated with crop development, rangeland biomass, and vegetation growth. Although various machine learning methods have been developed to forecast NDVI over short time ranges, such as one-month-ahead predictions, long-term forecasting approaches, such as one-year-ahead predictions of vegetation conditions, are not yet available. To fill this gap, we develop a two-phase machine learning model to forecast the one-year-ahead peak NDVI over high-resolution grids, using the Four Corners region of the southwestern United States as a testbed. In phase one, we identify informative climate attributes, including precipitation and maximum vapor pressure deficit, and develop the generalized parallel Gaussian process that captures the relationship between climate attributes and NDVI. In phase two, we forecast these climate attributes using historical data at least one year before the NDVI prediction month, which then serve as inputs to forecast the peak NDVI at each spatial grid. We develop open-source data and tools that outperform alternative methods for both gross NDVI and grid-based NDVI one-year forecasts, providing information that can help farmers and ranchers make actionable plans a year in advance.

In situations of uncertainty, people often make decisions with heuristic shortcuts or decision rules, rather than using computational or logical methods such as optimizing their behavior based on specific goals. The high level of uncertainty and complexity involved in adapting to climate change suggests that heuristics would be commonly used in this context rather than more structured decision methods. Through a systematic review of 137 articles, from 2007-2017 we explore the behavioral and cognitive assumptions used to examine agricultural decision-making related to climate change among farmers in developing countries. We find a strong orientation toward modeling behavior and decision making as a rational utility-maximizing process, despite decades of research demonstrating the prevalence of simpler heuristic choice when facing uncertainty and real-world constraints. Behavioral and cognitive approaches can increase our ability to predict or explain decisions being made in this realm, particularly in terms of how we understand decision making around information processing and risk assessment. In the following review, we highlight articles that have contributed to developing a more realistic decision-making framework for studying this problem on the ground. While there is a burgeoning literature using psychological insights to examine decision making under climate uncertainty, few studies consider the prevalence of simple heuristics, the presence of cognitive biases, and the salience of climate relative to other risk factors.