Publications

Growing demand for food coupled with climate commitments to reduce emissions will result in more land development for agriculture and renewable energy. Simultaneously, conserving land for biodiversity and nature’s contributions to people (NCP) is imperative for achieving international climate, sustainable development, and biodiversity goals. Meeting these interconnected objectives requires efficient land allocation across sectors. Here, we present a flexible, multiple-objective framework for strategically allocating land to mitigate threats to biodiversity and NCP under climate change while supporting development. Application of this framework at a global scale through country-level targets shows that if future development is planned without consideration of nature, demands for land could impact nearly 1 million km2 of high-priority conservation areas. Multi-sector planning can mitigate potential conflict, reducing carbon loss and species exposure. Our findings underscore the need to conserve critical areas for nature, reduce land demand for food and energy, and intentionally coordinate land use across sectors.

Farm size is a key characteristic of agricultural systems, closely linked to farming and land management practices. However, data on farm size are often difficult to obtain, especially in smallholder-dominated regions, and where available, they are rarely spatially explicit. This study examines the use of field size as a proxy for farm size, aiming to identify field size thresholds that can distinguish farms of different sizes. Using household-level data from Zambia’s Crop Forecast Survey, we applied both continuous and categorical methods to quantify the field-to-farm size relationship. Non-parametric Theil–Sen regression revealed a generally linear relationship between log-transformed field and farm sizes, particularly at the survey enumeration area scale, where farm size is proportional to field size (slope ≈ 1) and field size alone predicts farm size with a median bias of 0.017 ha. A Gaussian Naive Bayes classifier was also developed to assign fields to three categories: very small (A: < 2 ha), small (B: 2–5 ha), and medium (C: 5–20 ha), and a coarser two-class scheme (S: < 5 ha; M: 5–20 ha) that has been applied in previous studies. The Gaussian model identified field size thresholds at 0.58–0.6 ha (A–B) and 1.18–1.69 ha (B–C), depending on the prior farm size distribution. For the S–M scheme, thresholds ranged from 0.96–1.4 ha. Using these thresholds, national classification achieved F1 scores of 0.78, 0.59, and 0.77 for A, B, and C, respectively, with B more prone to misclassification. The two-class model achieved 0.87 and 0.88 for S and M, indicating more robust classification performance under a coarser scheme. Kernel density analysis shows that field-size distributions within farm categories and the separability of these distributions, vary across regions, with the underlying factors responsible for these differences remaining an open question for future research. These results demonstrate the potential of field size as a spatial proxy for farm size to support remote sensing based farming system classification in data-scarce regions.

Carbon finance initiatives such as Reducing Deforestation and Forest Degradation (REDD+), designed to mitigate climate change, offer an opportunity to also protect biodiversity. However, managing forests to store and sequester carbon does not necessarily conserve biodiversity. We evaluated the biodiversity co-benefits of the Gola-REDD+ initiative in the tropical forests of Sierra Leone, using bioacoustics and DNA metabarcoding under a quasi-experimental study design. We used soundscape saturation (SS) as a measure of vocalizing diversity, and e-DNA arthropod community as a complementary measure of biodiversity to examine whether a Gola-REDD+ financed protected area (Treatment-PA) conserved biodiversity more than (1) a multiuse community land (Control-CL) and (2) a PA without REDD+ finance (Control-PA). We found that REDD+ financing is associated with additional biodiversity co-benefits in the Treatment-PA compared to both control areas. Our study makes three key contributions. First, we provide concrete evidence on a carbon finance (REDD+) project's effectiveness in conserving faunal diversity while sequestering carbon. Second, we present a gold-standard causal inference study design for evaluating biodiversity co-benefits of conservation strategies. Third, we highlight the role of conservation technologies like bioacoustics and DNA metabarcoding in informing conservation policy.

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.

Rigorous evidence detailing the myriad interconnections between humans and ecosystems will be critical to slow the loss of biodiversity. Effective conservation interventions will depend upon a detailed understanding of the benefits that biodiversity provides to people, the ways that human activities drive biodiversity decline, and the potential for conservation policies to stem this decline. Although hundreds of papers explore these relationships, a careful review of this literature shows that the overwhelming majority of studies fall short of documenting causal relationships with biodiversity per se. However, a combination of data and methodological advances has led to rapid growth of quasi-experimental analyses that are advancing our understanding of human interactions with biodiversity in three domains. First, economists have provided valuable insights into the causes of biodiversity loss, which complement traditional ecological experiments by better reflecting real-world conditions. Second, quasi-experimental studies have begun to identify which policy interventions, in what contexts, have slowed biodiversity loss. Finally, recent quasi-experimental studies have shown that the loss of species can impose extremely large costs on humanity but that these costs vary widely depending upon the species and opportunities for human adaptation.

Forests are central to climate, biodiversity, and development goals, but effective monitoring and evaluation of their contributions depends on reliable data. Satellite-derived global forest cover and change datasets (GFDs) are widely used to address this need. However, differences in resolution, forest definition, and methodology challenge their use in research and policy, yet how GFD differences affect key forest-related estimates remains poorly understood. Here, we quantify global area-based spatial congruence among 10 GFDs and test their influence on three national policy-relevant estimates: carbon accounting, forest-poverty mapping, and biodiversity habitat. We find only 26% spatial congruence among GFDs at native resolution. This low congruence translates to an order-of-magnitude difference in national case study indicator estimates. We demonstrate that GFD selection fundamentally shapes monitoring and evaluation outcomes, particularly in biomes with fragmented or sparse tree cover. We provide a decision-support framework to guide GFD selection according to different forest-related science and policy applications.

Recent innovations in task-agnostic imagery featurization have lowered the computational costs of using machine learning to predict ground conditions from satellite imagery. These methods hold particular promise for the development of imagery-based monitoring systems in low-income regions, where data and computational resources can be limited. However, these relatively simple prediction pipelines have not been evaluated in developing-country contexts over time, limiting our understanding of their performance in practice. Here, we compute task-agnostic random convolutional features from satellite imagery and use linear ridge regression models to predict maize yields over space and time in Zambia, a country prone to severe droughts and crop failure. Leveraging Landsat and Sentinel 2 satellite constellations, in combination with district-level yield data, our model explains 83% of the out-of-sample maize yield variation from 2016 to 2021, slightly outperforming a model trained on Normalized Difference Vegetation Index (NDVI) features, a common remote sensing approach used by practitioners to monitor crop health. Our approach maintains an 𝑅2 score of 0.74 when predicting temporal variation alone, while the performance of the NDVI-based approach drops to an 𝑅2 of 0.39. Our findings imply that this task-agnostic featurization can be used to predict spatial and temporal variation in agricultural outcomes, even in contexts with limited ground truth data. More broadly, these results point to imagery-based monitoring as a promising tool for assisting agricultural planning and food security, even in contexts where computationally expensive methodologies remain out of reach.

As climate change makes agricultural production shocks more frequent and severe, it is vital to understand their effect on farmer welfare, land use, and deforestation. Theoretically, a change in agricultural productivity could increase or decrease deforestation by changing demand for agricultural land and/or through the consumption of forests as a coping strategy. This paper uses the introduction of a crop pest to sub-Saharan Africa to estimate the effect of a negative agricultural productivity shock on deforestation. Using primary household data, we first find that farmers who were exposed to higher levels of fall armyworm saw substantial decreases in yield and food security. Using estimates of fall armyworm suitability in conjunction with machine-learning models of maize yields and deforestation, we find that the introduction of the fall armyworm induced a doubling of the deforestation rate in Zambia in the 3 y following the outbreak. This increase was driven both by increased agricultural land expansion and increased charcoal production as a coping strategy. These responses vary substantially over space. More remote areas experienced 23% lower FAW-induced deforestation compared with the sample average, suggesting that farmers with access to maize and charcoal markets may have increased deforestation as a response. Wealthier areas were also less likely to deforest in response to FAW pressure. In sum, our results suggest that negative agricultural productivity shocks may lead to a negative climate feedback, with farmers engaging in emissions-increasing strategies in response.

Sustainability requirements imposed on agricultural producers by downstream supply chain actors risk excluding smallholder farmers from upgraded markets. Here we investigated smallholder participation in sustainably certified palm oil mill markets in Indonesia. We developed and applied a conceptual model to estimate the importance of structural market access, smallholder capacity, and buyer/seller behavior in shaping mill smallholder sourcing. Smallholders who hold exclusive contracts with specific mills were overrepresented at certified mills. Independent smallholders unaffiliated with mills contributed one-third of regional oil palm production but 7% of certified mill supply. We found no evidence that independent smallholders exited markets after mill certification (“active” exclusion). Instead, only 36% of certified mills ever purchased from independent smallholders, and independent smallholder lands were less common around certified (29–38% of palm area) versus noncertified (41–42%) mills. To address such “passive” exclusion, supply chain governance programs should encourage participation of actors well-positioned to source from small-scale producers.

Recent public and private policies seek to end deforestation by regulating the production and trade of forest-risk commodities. The design, implementation, and evaluation of these policies rely on metrics that are typically bounded in scope by either territories or supply chains, and therefore only provide a partial account of deforestation on the ground. We argue that metrics linking deforestation and forest degradation to commodity production need to consider two distinct questions: (1) How much of today’s commodity production is associated with past deforestation? and (2) to what extent is today’s deforestation driven by the prospects of producing a specific commodity in the future? This paper describes how metrics can respond to these questions by being classified according to their commodity or deforestation focus. We propose common terminology to facilitate the communication and use of these perspectives and metrics. We then make the case for combining perspectives through the monitoring and reporting of multiple metrics by governments, companies, and non-governmental organizations alike to both assess progress and drive more coordinated action to reduce deforestation.

As climate change increasingly affects biodiversity and ecosystem services, a key challenge in ecology is accurate attribution of these impacts. Though experimental studies have greatly advanced our understanding of climate change effects, experimental results are difficult to generalise to real-world scenarios. To better capture realised impacts, ecologists can use observational data. Disentangling cause and effect using observational data, however, requires careful research design. Here we describe advances in causal inference that can improve climate change attribution in observational settings. Our framework includes five steps: (1) describe the theoretical foundation, (2) choose appropriate observational datasets, (3) estimate the causal relationships of interest, (4) simulate a counterfactual scenario and (5) evaluate results and assumptions using robustness checks. We demonstrate this framework using a pinyon pine case study in North America, and we conclude with a discussion of frontiers in climate change attribution. Our aim is to provide an accessible foundation for applying observational causal inference to estimate climate change effects on ecological systems.

Climate change threatens global food systems1, but the extent to which adaptation will reduce losses remains unknown and controversial2. Even within the well-studied context of US agriculture, some analyses argue that adaptation will be widespread and climate damages small3,4, whereas others conclude that adaptation will be limited and losses severe5,6. Scenario-based analyses indicate that adaptation should have notable consequences on global agricultural productivity7,8,9, but there has been no systematic study of how extensively real-world producers actually adapt at the global scale. Here we empirically estimate the impact of global producer adaptations using longitudinal data on six staple crops spanning 12,658 regions, capturing two-thirds of global crop calories. We estimate that global production declines 5.5 × 1014 kcal annually per 1 °C global mean surface temperature (GMST) rise (120 kcal per person per day or 4.4% of recommended consumption per 1 °C; P < 0.001). We project that adaptation and income growth alleviate 23% of global losses in 2050 and 34% at the end of the century (6% and 12%, respectively; moderate-emissions scenario), but substantial residual losses remain for all staples except rice. In contrast to analyses of other outcomes that project the greatest damages to the global poor10,11, we find that global impacts are dominated by losses to modern-day breadbaskets with favourable climates and limited present adaptation, although losses in low-income regions losses are also substantial. These results indicate a scale of innovation, cropland expansion or further adaptation that might be necessary to ensure food security in a changing climate.

Agricultural land abandonment presents potential environmental benefits through either revegetation, and associated carbon sequestration, habitat and landscape connectivity benefits, or recultivation, which offsets the need for agricultural conversion of natural areas. Yet, the extent and pace of land abandonment depends on future demand for food, energy, and other anthropogenic drivers. Here we quantify the extent and spatial distribution of agricultural abandonment and conversion in the southeastern US under a range of future development scenarios, addressing (1) what is the extent of future agricultural abandonment and conversion, (2) how much forecast agricultural conversion could be offset by recultivation of abandoned land, and (3) within a given development scenario, how do different strategies for recultivation of abandoned lands influence (a) habitat fragmentation and (b) connectivity for the umbrella species Ursus americanus. Future abandonment ranged from 1.63 Mha (local economic scenario) to 7.95 Mha (local environmental scenario). Future conversion ranged from 1.24 Mha (global environmental scenario) to 5.65 Mha (global economic scenario). While environmental scenarios predicted surplus abandonment available to offset all conversion, economic scenarios predicted enough abandonment to offset a third of conversion at most. Within a given development scenario, strategic recultivation targeting carbon or biodiversity conservation can reduce fragmentation by up to 17% compared to land-use decisions that do not consider those characteristics. However, strategic recultivation did not significantly affect connectivity, which was instead driven by development scenarios: cost-weighted distance to least-cost path ratio was highest in the economic development scenarios and lowest in the environmental concern scenarios. Our results suggest that while socio-economic development scenarios are the primary drivers of land-use change patterns and the attendant ecological consequences, strategic recultivation decisions targeting carbon sequestration or biodiversity potential can reduce habitat fragmentation within development scenarios.

Forests in the Western United States face escalating threats from wildfire, pest outbreaks, and drought, leading experts and policymakers to call for extensive forest management to promote forest resilience and reduce wildfire risk. High treatment costs represent a major choke point to achieving forest management goals, but selling timber and biomass from forest thinning can offset costs and provide the revenue to help rapidly scale management actions. In this study, we assess how forest product market conditions influence the economic feasibility and scale of forest management by modeling treatment potential across a Northern Sierra Nevada landscape using the US Forest Service's BioSum tool. We evaluate treatment outcomes across nine economic scenarios, incorporating wood and biomass price variations and treatment subsidies. Results indicate that baseline pricing assumptions facilitate aggressive forest treatment where thinning is feasible and needed, but a 50 % drop in forest product prices led to a one-third decline in treated area. These reductions are completely offset by a $500/acre treatment subsidy, suggesting that subsidies could serve as a ‘price floor’ to maintain treatment levels through market fluctuations. Optimal, cost-effective treatments overwhelmingly utilized prescribed fire following thinning, emphasizing the role of fire-inclusive approaches for forest treatment. While study findings indicate that forest product markets can support landscape-scale treatments, the capacity of regional processing facilities currently limits full utilization of forest products, underscoring the importance of expanding wood and biomass utilization infrastructure to realize the potential of market-driven strategies for improving forest resilience in the Sierra Nevada and similar fire-prone regions.
 

Anticipating precipitation (PPT) extremes across sub-Saharan Africa can help mobilize interventions, trigger anticipatory actions, and promote beneficial actions like water harvesting. Reliable crop model forecasts can help identify when and where food aid interventions can be most beneficial. To date, however, there has been little research evaluating the utility of rainfall forecasts. This study, therefore, assesses the efficacy of the Subseasonal Consortium database (SubC) for use in a regional crop water balance model—the water requirement satisfaction index (WRSI)—in east Africa. We find that combining two dekads (20 d) of statistically downscaled and bias-corrected SubC PPT data with climatological information delivers improved estimates of end-of-season conditions over a 17 year test period. Our results show that SubC forecasts provide a 35%–55% reduction in EOS WRSI root mean squared error in 60% of the east African agropastoral areas during the short rains, with the highest accuracy being in areas that are most vulnerable to inconsistent PPT timing and quantities. Across the 17 tested seasons, 1999/00–2015/16, use of the SubC either improved or did not degrade the accuracy of WRSI prediction compared to a benchmark model for over 70% of the seasons and for 90% of the study region. In general, the improved accuracy provided by two dekads of SubC forecast is nearly equivalent to what can be attained with one dekad of a ‘perfect’ forecast (i.e. observation data). In effect, a 20 day forecast provides a 10 day advance in our early warning capabilities. During extreme events, such as during the 2005/2006 drought in east Africa, the SubC-driven WRSI could provide advanced warning of poor cropping conditions and potential crop failure up to 3 months before the end of the season. Overall, these improvements provide earlier and more accurate estimates of the likely seasonal water balance outcomes, and allow for the identification of locations where interventions may be needed.