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Abstract Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture’s hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California’s Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.

Abstract We are delighted to publish the names of the 613 peer reviewers who submitted 904 reviews in 2021. We express our deep appreciation for your thoughtful comments and suggestions on manuscripts submitted to Earth's Future.

Accurate and operational indicators of the start of growing season (SOS) are critical for crop modeling, famine early warning, and agricultural management in the developing world. Erroneous SOS estimates–late, or early, relative to actual planting dates–can lead to inaccurate crop production and food-availability forecasts. Adapting rainfed agriculture to climate change requires improved harmonization of planting with the onset of rains, and the rising ubiquity of mobile phones in east Africa enables real-time monitoring of this important agricultural decision. We investigate whether antecedent agro-meteorological variables and household-level attributes can be used to predict planting dates of small-scale maize producers in central Kenya. Using random forest models, we compare remote estimates of SOS with field-level survey data of actual planting dates. We compare three years of planting dates (2016–2018) for two rainy seasons (the October-to-December short rains, and the March-to-May long rains) gathered from weekly Short Message Service (SMS) mobile phone surveys. In situ data are compared to SOS from the Water Requirement Satisfaction Index (SOSWRSI) and other agro-meteorological variables from Earth observation (EO) datasets (rainfall, NDVI, and evaporative demand). The majority of farmers planted within 20 days of the SOSWRSI from 2016 to 2018. In the 2016 long rains season, many farmers reported planting late, which corresponds to drought conditions. We find that models relying solely on EO variables perform as well as models using both socio-economic and EO variables. The predictive accuracy of EO variables appears to be insensitive to differences in reference periods that were tested for deriving EO anomalies (1, 3, 5, or 10 years). As such, it would appear that farmers are either responding to short-term weather conditions (e.g., intra-seasonal variability), or longer trends than were included in this study (e.g., 25–30 years), when planting. The methodologies used in this study, weekly SMS surveys, provide an operational means for estimating farmer behaviors–information which is traditionally difficult and costly to collect.

Agricultural supply chains play a crucial role in supporting food security in Africa. However, high-resolution supply chain information is often not available, which hinders our ability to determine which interventions in food supply chains would most enhance food security. In this study, we develop a high-resolution supply chain model for essential staple crops in Zambia, aiming to estimate how improvements in transportation infrastructure would impact food security. Specifically, we simulate district-level monthly consumption, trade flows, and storage for maize and cassava in Zambia. We then conduct a counterfactual case study with low transportation costs, discovering that reducing transaction costs leads to higher aggregate net agricultural revenue and aggregate net expenditure. These results indicate that transportation investments are more beneficial to suppliers than to consumers, with implications for household food security in smallholder agriculture. Our study highlights the potential for infrastructure investments to improve food security.

Coastal marine ecosystems face a host of pressures from both offshore and land-based human activity. Research on terrestrial threats to coastal ecosystems has primarily focused on agricultural runoff, specifically showcasing how fertilizers and livestock waste create coastal eutrophication, harmful algae blooms, or hypoxic or anoxic zones. These impacts not only harm coastal species and ecosystems but also impact human health and economic activities. Few studies have assessed impacts of human wastewater on coastal ecosystems and community health. As such, we lack a comprehensive, fine-resolution, global assessment of human sewage inputs that captures both pathogens and nutrient flows to coastal waters and the potential impacts on coastal ecosystems. To address this gap, we use a new high-resolution geospatial model to measure and map nitrogen (N) and pathogen—fecal indicator organisms (FIO)—inputs from human sewage for ~135,000 watersheds globally. Because solutions depend on the source, we separate nitrogen and pathogen inputs from sewer, septic, and direct inputs. Our model indicates that wastewater adds 6.2Tg nitrogen into coastal waters, which is approximately 40% of total nitrogen from agriculture. Of total wastewater N, 63% (3.9Tg N) comes from sewered systems, 5% (0.3Tg N) from septic, and 32% (2.0Tg N) from direct input. We find that just 25 watersheds contribute nearly half of all wastewater N, but wastewater impacts most coastlines globally, with sewered, septic, and untreated wastewater inputs varying greatly across watersheds and by country. Importantly, model results find that 58% of coral and 88% of seagrass beds are exposed to wastewater N input. Across watersheds, N and FIO inputs are generally correlated. However, our model identifies important fine-grained spatial heterogeneity that highlight potential tradeoffs and synergies essential for management actions. Reducing impacts of nitrogen and pathogens on coastal ecosystems requires a greater focus on where wastewater inputs vary across the planet. Researchers and practitioners can also overlay these global, high resolution, wastewater input maps with maps describing the distribution of habitats and species, including humans, to determine the where the impacts of wastewater pressures are highest. This will help prioritize conservation efforts.Without such information, coastal ecosystems and the human communities that depend on them will remain imperiled.

Seasonal climate variability frequently undermines farm yields, reduces food availability, and lowers income. This is particularly evident among small-scale agricultural producers in both irrigated and non-irrigated agroecosystems in the Global South where maize cultivars constitute a critical component of food production. In these systems, farmers make climate-sensitive decisions that include the selection of late- and/or early-maturing seed varieties, the diversity of seed varieties sown, and when to plant. Farmers’ expectations of future rainfall would therefore seem to be critical determinants of agricultural outcomes and foreshadow climate impacts. However, few studies have quantified the linkages between on-farm decisions and farmer seasonal predictions. We report on detailed household and phone surveys of 501 smallholder farmers in central Kenya based on the 2018 growing seasons and expectations for the 2019 March-April-May growing season. We show that farmers’ expectations of the upcoming seasonal rainfall have important associations with selections of seed maturity varieties and the number of maturing varieties farmers expect to plant and less important associations with the seeds’ planting dates. Furthermore, we show that 79% of the farmers form an expectation of the future seasonal climate and about two-thirds of them formed expectations based on a heuristic that connects the past climate to future seasonal conditions. More problematically, one-third of the farmers formed their rainfall expectation based on the prior season, and we show that no such correlation exists in observational data nor is correlation of seasonal rainfall supported by current understanding of climate variability. These results highlight the challenges farmers face in anticipating seasonal rainfall, which has implications for crop diversification and choices to adopt drought tolerant cultivars. The results suggest that farmers’ expectations of upcoming seasonal climate are important measures of farm decision-making.

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers’ economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers’ participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of ‘plant water stress’ as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments’ financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

The common hippopotamus, Hippopotamus amphibius, transports millions of tons of organic matter annually from its terrestrial feeding grounds into aquatic habitats. We evaluated whether carbon stable isotopes (δ13C) can be used as tracers for determining whether H. amphibius‐vectored allochthonous material is utilized by aquatic consumers. Two approaches were employed to make this determination: (1) lab‐based feeding trials where omnivorous river fish were fed a H. amphibius dung diet and (2) field sampling of fish and aquatic insects in pools with and without H. amphibius. Lab trials revealed that fish fed exclusively H. amphibius dung exhibited significantly more positive δ13C values than fish not fed dung. Fish and aquatic insects sampled in a river pool used for decades by H. amphibius also exhibited more positive δ13C values at the end of the dry season than fish and insects sampled from an upstream H. amphibius‐free reference pool. Fish sampled in these same pools at the end of the wet season (high flow) showed no significant differences in δ13C values, suggesting that higher flows reduced retention and use of H. amphibius subsidies. These data provide preliminary evidence that δ13C values may be useful, in certain contexts, for quantifying the importance H. amphibius organic matter.

Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa’s urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa’s urban settlements distributions fit power law distributions exhibited by Zipf’s law. Our results reveal that 77%–85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa’s urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small- and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa’s ever-growing urban settlements.

Effects of agricultural practices on ecosystem carbon storage have acquired widespread concern due to its alleviation of rising atmospheric CO2 concentrations. Recently, combining of furrow-ridge with plastic film mulching in spring maize ecosystem was widely applied to boost crop water productivity in the semiarid regions of China. However, there is still limited information about the potentials for increased ecosystem carbon storage of this tillage method. The objective of this study was to quantify and contrast net carbon dioxide exchange, biomass accumulation and carbon budgets of maize (Zea maize L.) fields under the traditional non-mulching with flat tillage (CK) and partial plastic film mulching with furrow-ridge tillage (MFR) on the China Loess Plateau. Half-hourly net ecosystem CO2 exchange (NEE) of both treatments were synchronously measured with two eddy covariance systems during the growing seasons of 2011 through 2013. At same time green leaf area index (GLAI) and biomass were also measured biweekly. Compared with CK, the warmer and wetter (+1.3°C and +4.3%) top soil at MFR accelerated the rates of biomass accumulation, promoted greater green leaf area and thus shortened the growing seasons by an average value of 10.4 days for three years. MFR stimulated assimilation more than respiration during whole growing season, resulting in a higher carbon sequestration in terms of NEE of -79 gC/m2 than CK. However, after considering carbon in harvested grain (or aboveground biomass), there is a slight higher carbon sink (or a stronger carbon source) in MFR due to its greater difference of aboveground biomass than that of grain between both treatments. These results demonstrate that partial plastic film mulched furrow-ridge tillage with aboveground biomass exclusive of grain returned to the soil is an effective way to enhance simultaneously carbon sequestration and grain yield of maize in the semiarid regions.