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Rapidly rising populations and likely increases in incomes in sub-Saharan Africa make tens of millions of hectares of cropland expansion nearly inevitable, even with large increases in crop yields. Much of that expansion is likely to occur in higher rainfall savannas, with substantial costs to biodiversity and carbon storage. Zambia presents an acute example of this challenge, with an expected tripling of population by 2050, good potential to expand maize and soya bean production, and large areas of relatively undisturbed miombo woodland and associated habitat types of high biodiversity value. Here, we present a new model designed to explore the potential for targeting agricultural expansion in ways that achieve quantitatively optimal trade-offs between competing economic and environmental objectives: total converted land area (the reciprocal of potential yield); carbon loss, biodiversity loss and transportation costs. To allow different interests to find potential compromises, users can apply varying weights to examine the effects of their subjective preferences on the spatial allocation of new cropland and its costs. We find that small compromises from the objective to convert the highest yielding areas permit large savings in transportation costs, and the carbon and biodiversity impacts resulting from savannah conversion. For example, transferring just 30% of weight from a yield-maximizing objective equally between carbon and biodiversity protection objectives would increase total cropland area by just 2.7%, but result in avoided costs of 27-47% for carbon, biodiversity and transportation. Compromise solutions tend to focus agricultural expansion along existing transportation corridors and in already disturbed areas. Used appropriately, this type of model could help countries find agricultural expansion alternatives and related infrastructure and land use policies that help achieve production targets while helping to conserve Africa's rapidly transforming savannahs.This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'.

Savannah ecosystems are important carbon stocks on the Earth, and their quantification is crucial for understanding the global impact of climate and land-use changes in savannahs. The estimation of aboveground/belowground plant biomass requires tested allometric relationships that can be used to determine total plant biomass as a function of easy-to-measure morphological indicators. Despite recent advances in savannah ecology, research on allometric relations in savannahs remains confined to a few site-specific studies where basal area is typically used as the main morphometric parameter with plant biomass. We investigate allometric relations at four sites along a 950-km transect in the Kalahari across mean rainfall gradient 170 mm yr-1-550 mm yr-1. Using data from 342 harvested trees/shrubs, we relate basal area, height and crown diameter to aboveground biomass. These relationships are strongest in trees and weakest in small shrubs. Strong allometric relationships are also determined for morphologically similar groups of woody vegetation. We show that crown diameter can be used as an alternative to basal area in allometric relationships with plant biomass. This finding may enhance the ability to determine aboveground biomass over large areas using high-resolution aerial or satellite imagery without requiring ground-based measurements of basal area. © 2013 John Wiley & Sons Ltd.

Mapping the characteristics of Africa's smallholder-dominated croplands, including the sizes and numbers of fields, can provide critical insights into food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because there is 1) a spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) a lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, and used it to map Ghana's croplands. To overcome the spatio-temporal mismatch, we converted daily, high resolution imagery into two cloud-free composites (the primary growing season and subsequent dry season) covering the 2018 agricultural year, providing a seasonal contrast that helps to improve classification accuracy. To address the problem of label availability, we created a platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. Minimizing label errors improved model F1 scores by up to 25%. Active learning increased F1 scores by an average of 9.1% between first and last training iterations, and 2.3% more than models trained with randomly selected labels. We used the resulting 3.7 m map of cropland probabilities within a segmentation algorithm to delineate crop field boundaries. Using an independent map reference sample ( n = 1,207), we found that the cropland probability and field boundary maps had respective overall accuracies of 88 and 86.7%, user's accuracies for the cropland class of 61.2 and 78.9%, and producer's accuracies of 67.3 and 58.2%. An unbiased area estimate calculated from the map reference sample indicates that cropland covers 17.1% (15.4-18.9%) of Ghana. Using the most accurate validation labels to correct for biases in the segmented field boundaries map, we estimated that the average size and total number of field in Ghana are 1.73 ha and 1,662,281, respectively. Our results demonstrate an adaptable and transferable approach for developing annual, country-scale maps of crop field boundaries, with several features that effectively mitigate the errors inherent in remote sensing of smallholder-dominated agriculture., Competing Interests: JM, AHA, BWK, JM, BKM, IMM, JM, AMM, JMM, FWM, FMM, JO, CJO, PO, TW, and JGW were employed by company SpatialCollective. SD was employed by Uber ATG, at the time of the study. The company Uber ATG was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Estes, Ye, Song, Luo, Eastman, Meng, Zhang, McRitchie, Debats, Muhando, Amukoa, Kaloo, Makuru, Mbatia, Muasa, Mucha, Mugami, Mugami, Muinde, Mwawaza, Ochieng, Oduol, Oduor, Wanjiku, Wanyoike, Avery and Caylor.)

Smallholder farmers undertake a number of strategies to cope with climate shocks in a community. The sharing of resources across households constitutes one coping mechanism when environmental shocks differentially impact households. This paper investigates commodity sharing dynamics among households in eight communities in an environmentally heterogeneous highland-lowland area in central Kenya. We use survey data and meteorological data to test whether commodity sharing, measured at the household level by net inflow of commodities, varies across a regional precipitation gradient, and we reveal how sharing fluctuates with rainfall over the course of a year. We find both precipitation and income to be significant predictors of households' net value of shared commodities. Specifically, farmers who live in drier areas with less income are more likely to receive more commodities than they give. We also find that the length of time a household has been established in the area is significantly related to commodity sharing. Further, commodity sharing follows the pattern of harvest and food storage over the course of the year, with households giving the most commodities at times when food storage levels are higher, that is, post-harvest. The study sheds light on the relationship between commodity sharing as a coping mechanism and environmental heterogeneity in a region prone to seasonal food insecurity., Competing Interests: The authors have declared that no competing interests exist.

Foliar uptake of water from the surface of leaves is common when rainfall is scarce and non-meteoric water such as dew or fog is more abundant. However, many species in more mesic environments have hydrophobic leaves that do not allow the plant to uptake water. Unlike foliar uptake, all species can benefit from dew- or fog-induced transpiration suppression, but despite its ubiquity, transpiration suppression has so far never been quantified. Here, we investigate the effect of dew-induced transpiration suppression on the water balance and the isotope composition of leaves via a series of experiments. Characteristically, hydrophobic leaves of a tropical plant, Colocasia esculenta, are misted with isotopically enriched water to reproduce dew deposition. This species does not uptake water from the surface of its leaves. We measure leaf water isotopes and water potential and find that misted leaves exhibit a higher water potential and a more depleted water isotope composition than dry leaves, suggesting a ∼ 30% decrease in transpiration rate compared to control leaves. We propose three possible mechanisms governing the interaction of water droplets with leaf energy balance: increase in albedo from the presence of dew droplets, decrease in leaf temperature from the evaporation of dew, and local decrease in vapor pressure deficit. Comparing previous studies on foliar uptake to our results, we conclude that transpiration suppression has an effect of similar amplitude, yet opposite sign to foliar uptake on leaf water isotopes.

Spatial patterns of leaf water isotopes are challenging to predict because of the intricate link between vein and lamina water. Many models have attempted to predict these patterns, but to date, most have focused on monocots with parallel veins. These provide a simple system to study, but do not represent the majority of plant species. Here, a new protocol is developed using a Picarro induction module coupled to a cavity ringdown spectrometer to obtain maps of the leaf water isotopes ( 18 O and 2 H). The technique is applied to Colocasia esculenta leaves. The results are compared with isotope ratio mass spectrometry. In C. esculenta, a large enrichment in the radial direction is observed, but not in the longitudinal direction. The string-of-lakes model fails to predict the observed patterns, while the Farquhar-Gan model is more successful, especially when enrichment is accounted for along the radial direction. Our results show that reticulate-veined leaves experience a larger enrichment along the axis of the secondary veins than along the midrib. We hypothesize that this is due to the lower major/minor vein ratio that leads to longer pathways between major veins and sites of evaporation., (© 2017 John Wiley & Sons Ltd.)

Annual gross primary productivity (GPP) varies considerably due to climate-induced changes in plant phenology and physiology. However, the relative importance of plant phenology and physiology on annual GPP variation is not clear. In this study, a Statistical Model of Integrated Phenology and Physiology (SMIPP) was used to evaluate the relative contributions of maximum daily GPP (GPP max ) and the start and end of growing season (GS start and GS end ) to annual GPP variability, using a regional GPP product in North America during 2000-2014 and GPP data from 24 AmeriFlux sites. Climatic sensitivity of the three indicators was assessed to investigate the climate impacts on plant phenology and physiology. The SMIPP can explain 98% of inter-annual variability of GPP over mid- and high latitudes in North America. The long-term trend and inter-annual variability of GPP are dominated by GPP max both at the ecosystem and regional scales. During warmer spring and autumn, GS start is advanced and GS end delayed, respectively. GPP max responds positively to summer temperature over high latitudes (40-80°N), but negatively in mid-latitudes (25-40°N). This study demonstrates that plant physiology, rather than phenology, plays a dominant role in annual GPP variability, indicating more attention should be paid to physiological change under futher climate change., Competing Interests: The authors declare no competing financial interests.

Rationale: Volatile organic compounds (VOCs) such as methanol and ethanol in water extracted from plants cause spectral interference in isotope ratio infrared spectroscopy (IRIS). This contamination degrades the accuracy of measurements, limiting the use of IRIS. In response, this study presents a new decontamination method of VOCs for enhanced IRIS measurements., Methods: The isotopic compositions of water from laboratory-made and field-collected plant samples pre- and post-treatment were analyzed using IRIS. Traditional treatment methods of activated charcoal and commercial pre-combustion systems (MCM) were compared with our new treatment method that implements solid-phase extraction (SPE). The absolute concentrations of contaminants pre- and post-treatment were determined using (1)H and (13)C nuclear magnetic resonance to assess the effectiveness of the different treatments., Results: SPE removes an average of 86.7% and 78.8% ethanol and methanol, respectively, significantly reducing spectral interference. SPE reduces errors to within instrumental noise for both ethanol and methanol at concentrations found in nature (<3.0% and 0.08%, respectively). Activated charcoal minimally affected alcohol concentrations. MCM significantly worsened ethanol-contaminated water isotope measurements by producing primary alcohol oxidation products such as formic acid, another compound that interferes with IRIS absorption., Conclusions: SPE is an effective, low-cost method for eliminating errors in ethanol-contaminated samples. For samples where methanol is prevalent, combining SPE and MCM is more effective than the use of SPE alone. Hence, SPE treatment alone or in conjunction with MCM is recommended as an effective pre-analysis purification method for water extracted from plants., (Copyright © 2016 John Wiley & Sons, Ltd.)

Self-organized spatial vegetation patterning is widespread and has been described using models of scale-dependent feedback between plants and water on homogeneous substrates. As rainfall decreases, these models yield a characteristic sequence of patterns with increasingly sparse vegetation, followed by sudden collapse to desert. Thus, the final, spot-like pattern may provide early warning for such catastrophic shifts. In many arid ecosystems, however, termite nests impart substrate heterogeneity by altering soil properties, thereby enhancing plant growth. We show that termite-induced heterogeneity interacts with scale-dependent feedbacks to produce vegetation patterns at different spatial grains. Although the coarse-grained patterning resembles that created by scale-dependent feedback alone, it does not indicate imminent desertification. Rather, mound-field landscapes are more robust to aridity, suggesting that termites may help stabilize ecosystems under global change., (Copyright © 2015, American Association for the Advancement of Science.)

The contribution of savannas to global carbon storage is poorly understood, in part due to lack of knowledge of the amount of belowground biomass. In these ecosystems, the coexistence of woody and herbaceous life forms is often explained on the basis of belowground interactions among roots. However, the distribution of root biomass in savannas has seldom been investigated, and the dependence of root biomass on rainfall regime remains unclear, particularly for woody plants. Here we investigate patterns of belowground woody biomass along a rainfall gradient in the Kalahari of southern Africa, a region with consistent sandy soils. We test the hypotheses that (1) the root depth increases with mean annual precipitation (root optimality and plant hydrotropism hypothesis), and (2) the root-to-shoot ratio increases with decreasing mean annual rainfall (functional equilibrium hypothesis). Both hypotheses have been previously assessed for herbaceous vegetation using global root data sets. Our data do not support these hypotheses for the case of woody plants in savannas. We find that in the Kalahari, the root profiles of woody plants do not become deeper with increasing mean annual precipitation, whereas the root-to-shoot ratios decrease along a gradient of increasing aridity.

The metabolism of a river basin is defined as the set of processes through which the basin maintains its structure and responds to its environment. Green (or biotic) metabolism is measured via transpiration and blue (or abiotic) metabolism through runoff. A principle of equal metabolic rate per unit area throughout the basin structure is developed and tested in a river basin characterized by large heterogeneities in precipitation, vegetation, soil, and geomorphology. This principle is suggested to have profound implications for the spatial organization of river basin hydrologic dynamics, including the minimization of energy expenditure known to control the scale-invariant characteristics of river networks over several orders of magnitude. Empirically derived, remarkably constant rates of average transpiration per unit area through the basin structure lead to a power law for the probability distribution of transpiration from a randomly chosen subbasin. The average runoff per unit area, evaluated for subbasins of a wide range of topological magnitudes, is also shown to be remarkably constant independently of size. A similar result is found for the rainfall after accounting for canopy interception. Allometric scaling of metabolic rates with size, variously addressed in the biological literature and network theory under the label of Kleiber's law, is similarly derived. The empirical evidence suggests that river basin metabolic activity is linked with the spatial organization that takes place around the drainage network and therefore with the mechanisms responsible for the fractal geometry of the network, suggesting a new coevolutionary framework for biological, geomorphological, and hydrologic dynamics.

Determining the factors that influence the distribution of woody vegetation cover and resolving the sensitivity of woody vegetation cover to shifts in environmental forcing are critical steps necessary to predict continental-scale responses of dryland ecosystems to climate change. We use a 6-year satellite data record of fractional woody vegetation cover and an 11-year daily precipitation record to investigate the climatological controls on woody vegetation cover across the African continent. We find that-as opposed to a relationship with only mean annual rainfall-the upper limit of fractional woody vegetation cover is strongly influenced by both the quantity and intensity of rainfall events. Using a set of statistics derived from the seasonal distribution of rainfall, we show that areas with similar seasonal rainfall totals have higher fractional woody cover if the local rainfall climatology consists of frequent, less intense precipitation events. Based on these observations, we develop a generalized response surface between rainfall climatology and maximum woody vegetation cover across the African continent. The normalized local gradient of this response surface is used as an estimator of ecosystem vegetation sensitivity to climatological variation. A comparison between predicted climate sensitivity patterns and observed shifts in both rainfall and vegetation during 2009 reveals both the importance of rainfall climatology in governing how ecosystems respond to interannual fluctuations in climate and the utility of our framework as a means to forecast continental-scale patterns of vegetation shifts in response to future climate change.

The (18)O and (2)H of water vapor serve as powerful tracers of hydrological processes. The typical method for determining water vapor delta(18)O and delta(2)H involves cryogenic trapping and isotope ratio mass spectrometry. Even with recent technical advances, these methods cannot resolve vapor composition at high temporal resolutions. In recent years, a few groups have developed continuous laser absorption spectroscopy (LAS) approaches for measuring delta(18)O and delta(2)H which achieve accuracy levels similar to those of lab-based mass spectrometry methods. Unfortunately, most LAS systems need cryogenic cooling and constant calibration to a reference gas, and have substantial power requirements, making them unsuitable for long-term field deployment at remote field sites. A new method called Off-Axis Integrated Cavity Output Spectroscopy (OA-ICOS) has been developed which requires extremely low-energy consumption and neither reference gas nor cryogenic cooling. In this report, we develop a relatively simple pumping system coupled to a dew point generator to calibrate an ICOS-based instrument (Los Gatos Research Water Vapor Isotope Analyzer (WVIA) DLT-100) under various pressures using liquid water with known isotopic signatures. Results show that the WVIA can be successfully calibrated using this customized system for different pressure settings, which ensure that this instrument can be combined with other gas-sampling systems. The precisions of this instrument and the associated calibration method can reach approximately 0.08 per thousand for delta(18)O and approximately 0.4 per thousand for delta(2)H. Compared with conventional mass spectrometry and other LAS-based methods, the OA-ICOS technique provides a promising alternative tool for continuous water vapor isotopic measurements in field deployments., (Copyright 2009 John Wiley & Sons, Ltd.)

The concept of local-scale interactions driving large-scale pattern formation has been supported by numerical simulations, which have demonstrated that simple rules of interaction are capable of reproducing patterns observed in nature. These models of self-organization suggest that characteristic patterns should exist across a broad range of environmental conditions provided that local interactions do indeed dominate the development of community structure. Readily available observations that could be used to support these theoretical expectations, however, have lacked sufficient spatial extent or the necessary diversity of environmental conditions to confirm the model predictions. We use high-resolution satellite imagery to document the prevalence of self-organized vegetation patterns across a regional rainfall gradient in southern Africa, where percent tree cover ranges from 65% to 4%. Through the application of a cellular automata model, we find that the observed power-law distributions of tree canopy cluster sizes can arise from the interacting effects of global-scale resource constraints (that is, water availability) and local-scale facilitation. Positive local feedbacks result in power-law distributions without entailing threshold behaviour commonly associated with criticality. Our observations provide a framework for integrating a diverse suite of previous studies that have addressed either mean wet season rainfall or landscape-scale soil moisture variability as controls on the structural dynamics of arid and semi-arid ecosystems.

Tropical savannas commonly exhibit large spatial heterogeneity in vegetation structure. Fine-scale patterns of soil moisture, particularly in the deeper soil layers, have not been well investigated as factors possibly influencing vegetation patterns in savannas. Here we investigate the role of soil water availability and heterogeneity related to vegetation structure in an area of the Brazilian savanna (Cerrado). Our objective was to determine whether horizontal spatial variations of soil water are coupled with patterns of vegetation structure across tens of meters. We applied a novel methodological approach to convert soil electrical resistivity measurements along three 275-m transects to volumetric water content and then to estimates of plant available water (PAW). Structural attributes of the woody vegetation, including plant position, height, basal circumference, crown dimensions, and leaf area index, were surveyed within twenty-two 100-m(2) plots along the same transects, where no obvious vegetation gradients had been apparent. Spatial heterogeneity was evaluated through measurements of spatial autocorrelation in both PAW and vegetation structure. Comparisons with null models suggest that plants were randomly distributed over the transect with the greatest mean PAW and lowest PAW heterogeneity, and clustered in the driest and most heterogeneous transect. Plant density was positively related with PAW in the top 4 m of soil. The density-dependent vegetation attributes that are related to plot biomass, such as sum of tree heights per plot, exhibited spatial variation patterns that were remarkably similar to spatial variation of PAW in the top 4 m of soil. For PAW below 4 m depth, mean vegetation attributes, such as mean height, were negatively correlated with PAW, suggesting greater water uptake from the deep soil by plants of larger stature. These results are consistent with PAW heterogeneity being an important structuring factor in the plant distribution at the scale of tens of meters in this ecosystem.

* Increasing climatic unpredictability associated with characteristics of some species makes plant drought-tolerance an important drought-adaptation strategy. Using norm-of-reaction functions, or empirically determined functions that enable us to predict the state of a trait given the state of an environmental variable, allows modelling of plant performance when water availability varies randomly. * A mathematical model is proposed to evaluate drought-tolerance and growth strategies given a set of environmental parameters: the frequency of rainy days, the soil water-storage capacity, plant water use and plant growth rates. This model compares the performance of genotypes that differ in drought tolerance expressed as the ability to grow in drier soils, and assumes a general trade-off function between drought tolerance and maximum plant growth rate. * It is worth selecting plants with a greater degree of drought tolerance, expressed by the ability to grow in drier soils whenever the frequency of rains is smaller than the rate of soil water depletion. Otherwise, maximizing growth rate at the expense of drought tolerance is the best strategy. The nature of the trade-off between drought tolerance and plant growth rate also constrains the selection for optimal drought-adapted genotypes. * Breeders will have to consider these aspects of plant-environment interactions before establishing selection programs for drought adaptation.

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics.