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Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing

Biochar effect on maize yield and soil characteristics in five conservation farming sites in Zambia

Biochar addition to agricultural soils can improve soil fertility, with the added bonus of climate change mitigation through carbon sequestration. Conservation farming (CF) is precision farming, often combining minimum tillage, crop rotation and residue retention. In the present farmer-led field trials carried out in Zambia, the use of a low dosage biochar combined with CF minimum tillage was tested as a way to increase crop yields. Using CF minimum tillage allows the biochar to be applied to the area where most of the plant roots are present and mirrors the fertilizer application in CF practices. The CF practice used comprised manually hoe-dug planting 10-L sized basins, where 10%–12% of the land was tilled. Pilot trials were performed with maize cob biochar and wood biochar on five soils with variable physical/chemical characteristics. At a dosage as low as 4 tons/ha, both biochars had a strong positive effect on maize yields in the coarse white aeolian sand of Kaoma, West-Zambia, with yields of 444% ± 114% ( p = 0.06) and 352% ± 139% ( p = 0.1) of the fertilized reference plots for maize and wood biochar, respectively. Thus for sandy acidic soils, CF and biochar amendment can be a promising combination for increasing harvest yield. Moderate but non-significant effects on yields were observed for maize and wood biochar in a red sandy clay loam ultisol east of Lusaka, central Zambia (University of Zambia, UNZA, site) with growth of 142% ± 42% ( p > 0.2) and 131% ± 62% ( p > 0.2) of fertilized reference plots, respectively. For three other soils (acidic and neutral clay loams and silty clay with variable cation exchange capacity, CEC), no significant effects on maize yields were observed ( p > 0.2). In laboratory trials, 5% of the two biochars were added to the soil samples in order to study the effect of the biochar on physical and chemical soil characteristics. The large increase in crop yield in Kaoma soil was tentatively explained by a combination of an increased base saturation (from <50% to 60%–100%) and cation exchange capacity (CEC; from 2–3 to 5–9 cmol/kg) and increased plant-available water (from 17% to 21%) as well as water vapor uptake (70 mg/g on maize cob biochar at 50% relative humidity).

Agronomy Journal

Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat 30-m data, machine learning algorithms and Google Earth Engine

Accurate and timely information on croplands is important for environmental, food security, and policy studies. Spatially explicit cropland datasets are also required to derive information on crop type, crop yield, cropping intensity, as well as irrigated areas. Large area defined as continental to global cropland mapping is challenging due to differential manifestation of croplands, wide range of cultivation practices and limited reference data availability. This study presents the results of a cropland extent mapping of 64 Countriescovering large parts of Europe, Middle East, Russia and Central Asia. To cover such a vast area, roughly 160,000 Landsat scenes from 3,351 footprints between 2014 and 2016 were processed within the Google Earth Engine (GEE) cloud-platform. We used the pixel-based supervised Random Forest (RF) machine learning algorithm with a set of satellite data inputs capturing diverse spectral, temporal and topographical characteristics across twelve agroecological zones (AEZs). The reference data to train the classification model were collected from very high spatial resolution imagery (VHRI) and ancillary datasets. The result is a binary map showing cultivated/non-cultivated areas ca. 2015. The map produced an overall accuracy of 94 percent with roughly 14 percent omission and commission errors for the cropland class based on a large set of independent validation samples. The map suggests the entire study area has a total 546 million hectares (Mha) of croplands occupying 18 percent of the land area. Comparison between national cropland area estimates from United Nations Food and Agricultural Organizations (FAO) and those derived from this work also showed an R-square value of 0.95. For the entire Landsat-derived 30-m product the overall accuracy was 93.8% with cropland class providing producers accuracy of 86.5% (errors of omissions = 13.5%) and users accuracy of 85.7% (errors of commissions = 14.3%). This Landsat-derived 30-m cropland product (GFSAD30) provided 10-30% greater cropland areas compared to UN FAO in the 64 Countries. Finally, the map-to-map comparison between GFSAD30 with several other cropland products revealed that the best similarity matrix was with the 30m global land cover (GLC30) product providing an overall accuracy of 88.8 percent (Kappa 0.7) with producers cropland similarity of 89.2 percent (errors of omissions = 10.8%) and users cropland similarity of 81.8 percent (errors of commissions = 8.1%). GFSAD30 captured the missing croplands in GLC30 product around significantly irrigated agricultural areas in Germany and Belgium and rainfed agriculture in Italy. This study also established that the real strength of GFSAD30 product, compared to other products, were in: 1. Identifying precise location of croplands, and 2. Capturing fragmented croplands. The cropland extent map dataset is available through NASAs Land Processes Distributed Active Archive Center (LP DAAC) at https://doi.org/10.5067/MEaSUREs/GFSAD/GFSAD30EUCEARUMECE.001, while the training and reference data as well as visualization are available at the Global Croplands website.

ISPRS Journal of Photogrammetry and Remote Sensing

A customized framework for regional classification of conifers using automated feature extraction

Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m 2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods

Oregon, California, Nevada, Utah

A comparison of models for estimating the riverine export of nitrogen from large watersheds

We evaluated the accuracy of six watershed models of nitrogen export in streams (kg km2 yr-1) developed for use in large watersheds and representing various empirical and quasi-empirical approaches described in the literature. These models differ in their methods of calibration and have varying levels of spatial resolution and process complexity, which potentially affect the accuracy (bias and precision) of the model predictions of nitrogen export and source contributions to export. Using stream monitoring data and detailed estimates of the natural and cultural sources of nitrogen for 16 watersheds in the northeastern United States (drainage sizes = 475 to 70,000 km2), we assessed the accuracy of the model predictions of total nitrogen and nitrate-nitrogen export. The model validation included the use of an error modeling technique to identify biases caused by model deficiencies in quantifying nitrogen sources and biogeochemical processes affecting the transport of nitrogen in watersheds. Most models predicted stream nitrogen export to within 50% of the measured export in a majority of the watersheds. Prediction errors were negatively correlated with cultivated land area, indicating that the watershed models tended to over predict export in less agricultural and more forested watersheds and under predict in more agricultural basins. The magnitude of these biases differed appreciably among the models. Those models having more detailed descriptions of nitrogen sources, land and water attenuation of nitrogen, and water flow paths were found to have considerably lower bias and higher precision in their predictions of nitrogen export.

Biogeochemistry

A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km 2 ), about 461,000 km 2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

International Journal of Applied Earth Observation

Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning

Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km 2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.

California

Maize yield forecasts for Sub-Saharan Africa using Earth Observation data and machine learning

Food insecurity continues to grow in Sub-Saharan Africa (SSA). In 2019, chronically malnourished people numbered nearly 240 million, or 20% of the population in SSA. Globally, numerous efforts have been made to anticipate potential droughts, crop conditions, and food shortages in order to improve early warning and risk management for food insecurity. To support this goal, we develop an Earth Observation (EO) and machine-learning-based operational, subnational maize yield forecast system and evaluate its out-of-sample forecast skills during the growing seasons for Kenya, Somalia, Malawi, and Burkina Faso. In general, forecast skills improve substantially during the vegetative growth period (VP) and gradually during the reproductive development period (RP). Thus, mid-season assessment can provide effective early warning months before harvest. Skillful forecasts (Nash Sutcliffe Efficiency (NSE) > 0.6 and Mean Absolute Percentage Error (MAPE) < 20%) appear approximately two dekads after the VP; for example, skillful forecasts appear in May in Kenya and Somalia, January in Malawi, and July in Burkina Faso. During model development, effective EO features are also identified, such as precipitation and available water during VP, and dry days and extreme temperatures in early VP. Compared to monthly standard EO features, sub-monthly (dekadal), non-standard, and serial EO features significantly improve forecast skills by + 0.3 NSE and -10% of MAPE, demonstrating the ability to precisely and effectively capture favorable or detrimental crop development conditions. Finally, skillful forecasts and practical utility are demonstrated in the recent normal and dry years in each region. Overall, the developed yield forecasting system can provide skillful predictions during the growing season, supporting regional and international agricultural decision-making processes, including informing food-security planning and management, thereby helping to mitigate food shortages caused by unfavorable climate conditions.

Global Food Security

Concentrations, and estimated loads and yields of total nitrogen and total phosphorus at selected water-quality monitoring network stations in Kentucky, 1979-2004

To evaluate the State’s water quality, the Kentucky Division of Water collects data from a statewide network of primary ambient stream water-quality monitoring stations and flexible, rotating watershed-monitoring stations. This ambient stream water-quality monitoring network program is directed to assess the conditions of surface waters throughout Kentucky. Water samples were collected monthly for the majority of the stations from 1979 to 1998, which represented agricultural, undeveloped (mainly forested), and areas of mixed land use/land cover. In 1998, the number of water samples collected was reduced to a collection frequency of six times per year (every 2 months) every 4 of 5 years, because a new monitoring network was implemented involving a 5-year rotating Basin Management Unit scheme of monitoring. This report presents the results of a study conducted by the U.S. Geological Survey, in cooperation with the Kentucky Energy and Environment Cabinet–Kentucky Division of Water, to summarize concentrations of total nitrogen and total phosphorus and provide estimates of total nitrogen and total phosphorus loads and yields in 55 selected streams in Kentucky’s ambient stream water-quality monitoring network, which was operated from 1979 through 2004. Streams in predominately agricultural basins had higher concentrations of total nitrogen (TN) and concentrations of total phosphorus (TP) than streams in predominately undeveloped (forested) basins. Streams in basins in intensely developed karst areas characterized by caves, springs, sinkholes, and sinking streams had a higher median concentration of TN (1.5 milligrams per liter [mg/L]) than streams in basins with limited or no karst areas (0.63 mg/L). As with TN, median concentrations of TP also were higher in areas of intense karst (0.05 mg/L) than in areas with limited or no karst (0.02 mg/L). The U.S. Environmental Protection Agency (USEPA) has recommended ecoregional nutrient water-quality criteria as a starting point for States to establish more precise numeric water-quality criteria for nutrients to protect aquatic life and recreational and other uses of rivers and streams. On the basis of the 25 th percentile of concentration data from reference stations aggregated by ecoregion, the USEPA established recommended water-quality criteria for TN and TP in the two Aggregated Ecoregions (IX and XI) in Kentucky waters. The 25 th percentile median values for TN and TP from this study exceeded the USEPA’s recommendations in both aggregated ecoregions in the agricultural and mixed land-use/land-cover basins, and for TN in the undeveloped land-use/land-cover basins in Aggregated Ecoregion XI. However, the 25 th percentile median values for TN (Aggregated Ecoregion IX) and TP in both aggregated ecoregions did not exceed the USEPA’s recommendations in the undeveloped land-use/land-cover basins. Estimated loads and yields of TN and TP varied substantially among the individual stations. Estimated mean annual yields of TN ranged from 0.10 [tons per year per square mile (ton/yr)/mi 2 ] to 7.2 (ton/yr)/mi 2 , and estimated mean annual yields of TP ranged from 0.02 (ton/yr)/mi 2 to 1.4 (ton/yr)/mi 2 . Estimated mean annual yields of TN and TP were generally highest at stations in predominately agricultural basins, and lowest at stations in undeveloped land-use/land-cover basins.

Kentucky

Molecular sequencing and morphological identification reveal similar patterns in native bee communities across public and private grasslands of eastern North Dakota

Bees play a key role in the functioning of human-modified and natural ecosystems by pollinating agricultural crops and wild plant communities. Global pollinator conservation efforts need large-scale and long-term monitoring to detect changes in species’ demographic patterns and shifts in bee community structure. The objective of this project was to test a molecular sequencing pipeline that would utilize a commonly used locus, produce accurate and precise identifications consistent with morphological identifications, and generate data that are both qualitative and quantitative. We applied this amplicon sequencing pipeline to native bee communities sampled across Conservation Reserve Program (CRP) lands and native grasslands in eastern North Dakota. We found the 28S LSU locus to be more capable of discriminating between species than the 18S SSU rRNA locus, and in some cases even resolved instances of cryptic species or morphologically ambiguous species complexes. Overall, we found the amplicon sequencing method to be a qualitatively accurate representation of the sampled bee community richness and species identity, especially when a well-curated database of known 28S LSU sequences is available. Both morphological identification and molecular sequencing revealed similar patterns in native bee community structure across CRP lands and native prairie. Additionally, a genetic algorithm approach to compute taxon-specific correction factors using a small subset of the most concordant samples demonstrated that a high level of quantitative accuracy could be possible if the specimens are fresh and processed soon after collection. Here we provide a first step to a molecular pipeline for identifying insect pollinator communities. This tool should prove useful for future national monitoring efforts as use of molecular tools becomes more affordable and as numbers of 28S LSU sequences for pollinator species increase in publicly-available databases.

North Dakota

Assessing the state of hydrologic science in the Upper Klamath Basin—A comprehensive review of data, tools, and models

Water demand in the Upper Klamath Basin (UKB) from various stakeholders and ecological needs often outstrips available supply, leading to persistent management challenges. This study reviews the state of hydrologic science within the UKB as of 2025—specifically, the tools, data, and models available for assessing five key components of the water system: (1) surface water; (2) precipitation; (3) evapotranspiration; (4) groundwater; and (5) water use. The UKB water supply is critical for Native American communities, regional agriculture, and federally listed fishes and faces challenges from competing needs, climate variability, and operational/regulatory requirements. We assess existing datasets, regional and national models, and historical studies to understand the available resources and identify gaps that may hinder integrated water assessments and management. Our findings indicate areas where improvements in data collection and model precision could improve the accuracy of water-availability forecasts and support water-management practices. This review can inform near-term forecasting, assist in optimizing water-resource data collection and management strategies, and support regional water-availability assessments of the basin.

Oregon

Groundwater-flow model of the Treasure Valley, southwestern Idaho, 1986–2015

Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. Current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost portion of the western Snake River Plain aquifer system, called the Treasure Valley. The development of the model was guided by several objectives, including: to improve the understanding of groundwater and surface water interactions; to facilitate conjunctive water management; to provide a tool for water resources planning; and to provide a tool for water allocation. The model was constructed with a spatial scale and level of detail that aimed to meet these objectives while balancing the sometimes-competing goals of fast runtimes, numerical stability, usability, and parsimony. The Treasure Valley Groundwater Flow Model (TVGWFM) is a three-dimensional finite-difference numerical model constructed using MODFLOW 6 (Langevin and others, 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55 ). The model covers the westernmost portion of the western Snake River Plain and is discretized into a regular grid of 64 by 65 cells with a side length of 1 mile and 6 layers of varying depth and active area. A historical model period was developed consisting of 360 month-long stress periods for 1986–2015. The model builds upon previous modeling efforts by adding a transient period, incorporating new head and discharge observations to constrain parameters, incorporating information from the hydrogeologic framework model (HFM) of Bartolino (2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon: U.S. Geological Survey Scientific Investigations Report 2019–5138, https://doi.org/10.3133/sir20195138 ) and incorporating refined estimates of evapotranspiration and irrigation classification of lands in the study area. The TVGWFM includes all significant components of recharge to and discharge from the aquifer. Inflows include canal seepage, irrigation and precipitation recharge, mountain-front recharge, rivers and stream seepage, and seepage from Lake Lowell. Outflows include discharge to agricultural drainage ditches, discharge to rivers and streams, pumping, and discharge to Lake Lowell. Each recharge or discharge component is represented separately using individual MODFLOW 6 packages. Parameter values were derived with a combination of trial-and-error steps and automated parameter estimation using PEST software (Doherty, J.E., 2005, PEST, model-independent parameter estimation–User manual: Watermark Numerical Computing, https://pesthomepage.org/documentation ). Parameter estimates were constrained with several types of observation data, including water levels, water level changes, vertical water level differences, drain discharges, change in drain discharges, river seepage, seepage from Lake Lowell, and change in seepage from Lake Lowell. Material properties from the hydrogeologic framework were also used to assign the minimum and maximum values of some parameters. A final parameter realization was reached that minimized residuals between the observed and modelled values for the various observation groups. Mean residuals for the observation groups were 15.4 feet (ft) for water levels, 0.2 ft for water level changes, 19.4 ft for vertical water level differences, −3.9 cubic feet per second (ft 3 /s) for drain discharges, 0.0 ft 3 /s for changes in drain discharge, 45.0 ft 3 /s for river seepage, −40.1 ft 3 /s for Lake Lowell seepage, and 126.3 ft 3 /s for changes in Lake Lowell seepage. The quality of the model’s fit to observations varied spatially, with notable areas of under- or over-simulation of water levels present to the northwest and southwest of Lake Lowell, in the foothills along the eastern model boundary, and near the City of Eagle. Trends were observed in the residuals of many of the observation groups, indicating that the model is missing or not fully reproducing some phenomena that are observed in the system. The TVGWFM can be used as a tool for water resource planning, for understanding the interactions of groundwater and surface water at a basin scale, and for facilitating conjunctive management, but may lack the precision needed for water rights administration at a local scale. Additional sources of uncertainty or limitations of the model are noted. The quantity and spatial distribution of canal seepage and infiltration of irrigation water recharge, the largest sources of recharge to the system, are unknown and approximated indirectly. There is poor understanding of how canal seepage and incidental recharge change as land is converted from agricultural (irrigated) to suburban (semi-irrigated). These uncertainties will affect any scenarios that investigate changes to land use or irrigation practices. Finally, the model has relatively high water-level residuals around and to the southwest of Lake Lowell and should not be used to estimate water level effects in that region. The model was built with multiple, broadly expressed objectives and did not optimize performance for specific uses. However, the model and the tools included in an associated data release provide ample flexibility to improve the model for future uses. Adjustments and improvements could be made by refining the model in an area of interest, collecting additional calibration data, applying more rigorous boundary conditions, or re-estimating model parameters to optimize model performance for a specific model forecast.

Idaho

Drought and water management in ancient Maya society

Paleoclimate research in the Maya region of Mesoamerica provides compelling evidence of drought during key periods of cultural transition in Maya society. These include the transition from the Preclassic to the Classic, and from Classic to the Postclassic. Previous research emphasized a causal relationship between drought and cultural change, or so-called “collapse” in the Maya region. Recent advances in the range and precision of climate-sensitive proxies and the development of new archives have enabled quantitative reconstructions of past hydroclimate, as well as providing evidence of high impact, short-duration events, such as tropical cyclones. Simultaneously, archaeological research has unearthed widespread evidence of technologies used by the Maya to exert control over water resources in urban, rural, and agricultural settings. Evidence suggests that many of these water features were in use for multiple generations, possibly centuries, and many were constructed during the Terminal Preclassic and Terminal Classic periods. We suggest that, given the availability of new archaeological and paleoclimate records, these data can be combined to identify the full complexity of Maya adaptation to hydroclimate variability to emphasize adaptation and resilience to both water scarcity and over-abundance (e.g., flooding). Such syntheses, which can offer lessons for present-day efforts to grapple with regional climate change, will benefit from additional studies in data-poor zones of the Maya region, as well as public archiving of paleoclimate and archaeological data.

Progress in Physical Geography: Earth and Environm

Crop water productivity from cloud-Based landsat helps assess California’s water savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m 3 ), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ET a ; mm/d) to determine Crop Water Use (CWU; m 3 /m 2 ), and 2. Crop Productivity (CP; kg/m 2 ) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m 2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m 3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant.

California

The South Florida Ecosystem Portfolio Model - A Map-Based Multicriteria Ecological, Economic, and Community Land-Use Planning Tool

The South Florida Ecosystem Portfolio Model (EPM) prototype is a regional land-use planning Web tool that integrates ecological, economic, and social information and values of relevance to decision-makers and stakeholders. The EPM uses a multicriteria evaluation framework that builds on geographic information system-based (GIS) analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to regional land-use/land-cover (LULC) change. The EPM uses both economics (monetized) and multiattribute utility (nonmonetized) approaches to valuing these endpoints and consequences. This hybrid approach represents a methodological middle ground between rigorous economic and ecological/ environmental scientific approaches. The EPM sacrifices some degree of economic- and ecological-forecasting precision to gain methodological transparency, spatial explicitness, and transferability, while maintaining credibility. After all, even small steps in the direction of including ecosystem services evaluation are an improvement over current land-use planning practice (Boyd and Wainger, 2003). There are many participants involved in land-use decision-making in South Florida, including local, regional, State, and Federal agencies, developers, environmental groups, agricultural groups, and other stakeholders (South Florida Regional Planning Council, 2003, 2004). The EPM's multicriteria evaluation framework is designed to cut across the objectives and knowledge bases of all of these participants. This approach places fundamental importance on social equity and stakeholder participation in land-use decision-making, but makes no attempt to determine normative socially 'optimal' land-use plans. The EPM is thus a map-based set of evaluation tools for planners and stakeholders to use in their deliberations of what is 'best', considering a balancing of disparate interests within a regional perspective. Although issues of regional ecological sustainability can be explored with the EPM (for example, changes in biodiversity potential and regional habitat fragmentation), it does not attempt to define or evaluate long-term ecological sustainability as such. Instead, the EPM is intended to provide transparent first-order indications of the direction of ecological, economic, and community change, not to make detailed predictions of ecological, economic, and social outcomes. In short, the EPM is an attempt to widen the perspectives of its users by integrating natural and social scientific information in a framework that recognizes the diversity of values at stake in South Florida land-use planning. For terrestrial ecosystems, land-cover change is one of the most important direct drivers of changes in ecosystem services (Hassan and others, 2005). More specifically, the fragmentation of habitat from expanding low-density development across landscapes appears to be a major driver of terrestrial species decline and the impairment of terrestrial ecosystem integrity, in some cases causing irreversible impairment from a land-use planning perspective (Brody, 2008; Peck, 1998). Many resource managers and land-use planners have come to realize that evaluating land-use conversions on a parcel-by-parcel basis leads to a fragmented and narrow view of the regional effects of natural land-cover loss to development (Marsh and Lallas, 1995). The EPM is an attempt to integrate important aspects of the coupled natural-system/human-system view from a regional planning perspective. The EPM evaluates proposed land-use changes, both conversion and intensification, in terms of relevant ecological, economic, and social criteria that combine information about probable land-use outcomes, based on ecological and environmental models, as well as value judgments, as expressed in user-modifiable preference models. Based on on-going meetings and interviews with stakeholders and potential tool users we foc

Scientific Investigations Report

Advancements towards selective barrier passage by automatic species identification: Applications of deep convolutional neural networks on images of dewatered fish

Invasive species negatively affect enterprises such as fisheries, agriculture, and international trade. In the Laurentian Great Lakes Basin, threats include invasive sea lamprey ( Petromyzon marinus ) and the four major Chinese carps. Barriers have proven to be an effective mechanism for managing invasive species but are detrimental in that they also limit the migration of desirable, native species. Fish passage technologies that selectively pass desirable species while blocking undesirable species are needed. Key to an automated selective barrier passage system is a high precision fish classifier to assign fish to be passed or blocked. Presented is an evaluation of two classifiers developed using images of partially dewatered fish captured from a commercial, high-speed camera array. For a lamprey vs. non-lamprey classification task, an ensemble prediction approach achieved near perfect accuracy on both a validation and test dataset. For a species classification task for 13 species found in the Great Lakes region, an ensemble prediction approach achieved accuracies of 96% and 97% on a validation and test dataset, respectively. Both prediction approaches were based on deep convolutional neural networks constructed using transfer learning and image augmentation. The study provides an important proof-of-concept for the viability in fully automated, selective fish passage systems.

Illinois, Michigan,Ohio

True metabolizable energy for wood ducks from acorns compared to other waterfowl foods

Acorns of bottomland red oaks (Quercus spp.) are an important food of North American wood ducks (Aix sponsa). Barras et al. (1996) demonstrated that female wood ducks selected willow oak (Q. phellos) acorns over other species. We measured true metabolizable energy (TME) derived by captive, wild-strain, adult female wood ducks from acorns of willow oak, water oak (Q. nigra), cherrybark oak (Q. pagoda), and pin oak (Q. palustris) to determine whether female wood ducks ' preference for willow oak acorns was related to TME. Estimates of TME within acorn species were relatively precise, yet we did not detect variation in TME among acorn species (P=0.31); hence, we estimated TME across species (2.76±0.033 [SE] kcal/g dry mass; n=34). We concluded that TME apparently did not explain female wood ducks ' preference for willow oak acorns and hypothesized that morphological characteristics of willow oak acorns may be proximate cues related to selection by wood ducks . We also summarized known TME estimates for acorns fed to wood ducks and mallards (Anas platyrhynchos), and natural and agricultural foods fed to mallards, northern pintails (A. acuta), blue-winged teal (A. discors), and Canada geese (Branta canadensis). We found that acorns and moist-soil plant seeds and tubers provided, on average, about 76% of the TME in agricultural seeds. Thus, bottomland-hardwood and moist-soil habitats have potential to provide significant amounts of dietary energy , as well as greater diversity of foods and nutrients than croplands. Researchers should continue to determine TME of common foods (plant and animal) of waterfowl , and use TME in estimating waterfowl habitat carrying capacity (e.g., Reinecke et al. 1989). Additionally, large-scale, reliable estimates of plant and animal food availability in bottomland-hardwood and moist-soil habitats are needed to evaluate carrying capacity of landscapes important to waterfowl , such as the Mississippi Alluvial Valley (MAV).

Journal of Wildlife Management

The LTAR Grazing Land Common Experiment at the Jornada Experimental Range: Old genetics, new precision technologies, and adaptive value chains

Rangelands and the supply chains connected to them are central to the agrifood systems of the Southwestern United States. Local ranchers are simultaneously arid lands managers, herd managers, and marketing managers. To stay in business, they must constantly adapt to unpredictable forage resources and markets while conserving soils and vegetation resources for the long term. As climate warming and drying exacerbate the complexity and difficulty of day-to-day production, producers and policy-makers are seeking alternatives to “business as usual”. To meet this need, the LTAR-Jornada team has developed a package of strategies to help producers adapt to the local and inter-regional challenges. The package includes heritage cattle, precision ranching systems, and adaptive value chains. Five ranches across the Southwest have adopted different combinations of the strategies and are partnering to measure their benefits and drawbacks in real-world conditions. Opportunities for controlled experimentation differ among the ranches, so we use LTAR’s indicator system to assess and compare results. Even as we invest in co-producing knowledge about these three strategies, we recognize that progressive aridification and urbanization of Southwestern rangelands create challenges for which a single “silver bullet” agricultural innovation is unlikely to provide durable solutions. We are learning from our customers and stakeholders about ways to adjust the development of new options.

New Mexico