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The return of crop residues to cultivated fields has numerous agronomic and soil quality benefits and, therefore, the areal extent of crop residue cover (CRC) could provide a rapid measure of the sustainability of agricultural production systems in a region. Recognizing the limitations of traditional CRC methods, a new method is proposed for estimating the spatial and temporal distribution of maize residue cover (MRC) in the Jilin Province, NE China. The method used random forest (RF) algorithms, 13 tillage indices and 9 textural feature indicators derived from Sentinel-2 data. The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). Among the texture features, the best-fitting was Band8AMean-5∗5 (R2 of 0.56 and 0.54 for the line-transect and photographic methods, respectively). Based on MSE and InNodePurity, the optimal combination of RF algorithm for the line-transect method was STI, NDTI, NDI7, NDRI5, SRNDI, NDRI6, NDRI7 and Band3Mean-3∗3. Likewise, the optimal combination of RF algorithm for the photographic method was STI, NDTI, NDI7, SRNDI, NDRI6, NDRI5, NDRI9 and Band3Mean-3∗3. Regional distribution of MRC in the Jilin Province, estimated using the RF prediction model, was higher in the central and southeast sections than in the northwest. That distribution was in line with the spatial heterogeneity of maize yield in the region. These findings showed that the RF algorithm can be used to map regional MRC and, therefore, represents a useful tool for monitoring regional-scale adoption of conservation agricultural practices.

Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture.

Lake water surface albedo (LWSA) is a critical parameter for describing energy flux balance at the air–water interface. It is used to measure the capacity of lake water surface reflectance in the shortwave radiation band, which controls the energy partitioning between atmosphere and lake. LWSA and its driving factors, including solar radiation angle, water quality and meteorology, in lakes of Northeast China was analyzed in this research. Solar altitude angle emerged as the most important controlling factor of LWSA. The results showed a strong reduction in LWSA values with increase in solar altitude angle. The study also documented a strong effect of suspended matter on the radiative properties of lakes surface, leading to increased LWSA with increased in suspended matter concentration, especially at high solar altitude angles (R2: 0.76). While no significant association (R2: 0.11) was found between LWSA and chlorophyll-a concentration, LWSA was positively correlated with wind speed (R2 of 0.62 and 0.50 at high and low solar altitude angles, respectively), likely a consequence of wind-driven sediment resuspension in lake waters. Although chromophoric dissolved organic matter (CDOM) and organic particulate matter can affect energy partitioning and spectral distribution of incident solar radiation in lake waters, there was insufficient evidence of measurable effects on LWSA. Variations in LWSA recorded in this study were primarily dependent on similar solar altitude angles and backscattering properties of inorganic particulate matter suspended in the water column. Higher surface albedo was found in the turbid lakes.

Abstract It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.

Tillage is the most common agricultural practice dating back to the origin of agriculture. In recent decades, no-tillage (NT) has been introduced to improve soil and water quality. However, changes in soil properties resulting from long-term NT can increase losses of dissolved phosphorus, nitrate and some classes of pesticides, and NT effect on nitrous oxide (N2O) emission remains controversial. Complementary management that enhances the overall environmental benefits of NT is therefore crucial. By incorporating cover crops, nutrient cycling and nutrient use efficiency in NT fields could be improved given the nutrient supplying capacity of some cover crops. Cover crops could also offset the need for occasional tillage of NT cropland, an operation whose effect is only temporary in reducing, for example, soil compaction associated with NT management. When used in combination with NT, cover crop termination methods, using agrochemicals, should be carefully considered to prevent further jeopardy to water quality. Compared to herbicides, the use of roller crimping could potentially result in production cost saving while minimizing soil disturbance and export of agrochemicals. Future research should focus on various combinations of cover crop traits (e.g., decomposition rate) and management (e.g., timing of cover crop termination) that account for site- and cash crop-specific requirements.

Drought has been a major cause of agricultural disaster, yet how it affects the vulnerability of maize and wheat production in combination with several co-varying factors (i.e., phenological phases, agro-climatic regions, soil texture) remains unclear. Using a data synthesis approach, this study aims to better characterize the effects of those co-varying factors with drought and to provide critical information on minimizing yield loss. We collected data from peer-reviewed publications between 1980 and 2015 which examined maize and wheat yield responses to drought using field experiments. We performed unweighted analysis using the log response ratio to calculate the bootstrapped confidence limits of yield responses and calculated drought sensitivities with regards to those co-varying factors. Our results showed that yield reduction varied with species, with wheat having lower yield reduction (20.6%) compared to maize (39.3%) at approximately 40% water reduction. Maize was also more sensitive to drought than wheat, particularly during reproductive phase and equally sensitive in the dryland and non-dryland regions. While no yield difference was observed among regions or different soil texture, wheat cultivation in the dryland was more prone to yield loss than in the non-dryland region. Informed by these results, we discuss potential causes and possible approaches that may minimize drought impacts.

Food legume crops play important roles in conservation farming systems and contribute to food security in the developing world. However, in many regions of the world, their production has been adversely affected by drought. Although water scarcity is a severe abiotic constraint of legume crops productivity, it remains unclear how the effects of drought co-vary with legume species, soil texture, agroclimatic region, and drought timing. To address these uncertainties, we collected literature data between 1980 and 2014 that reported monoculture legume yield responses to drought under field conditions, and analyzed this data set using meta-analysis techniques. Our results showed that the amount of water reduction was positively related with yield reduction, but the extent of the impact varied with legume species and the phenological state during which drought occurred. Overall, lentil (Lens culinaris), groundnut (Arachis hypogaea), and pigeon pea (Cajanus cajan) were found to experience lower drought-induced yield reduction compared to legumes such as cowpea (Vigna unguiculata) and green gram (Vigna radiate). Yield reduction was generally greater when legumes experienced drought during their reproductive stage compared to during their vegetative stage. Legumes grown in soil with medium texture also exhibited greater yield reduction compared to those planted on soil of either coarse or fine texture. In contrast, regions and their associated climatic factors did not significantly affect legume yield reduction. In the face of changing climate, our study provides useful information for agricultural planning and research directions for development of drought-resistant legume species to improve adaptation and resilience of agricultural systems in the drought-prone regions of the world.

Suspended particulate matter (SPM) in lakes exerts strong impact on light propagation, aquatic ecosystem productivity, which co-varies with nutrients, heavy metal and micro-pollutant in waters. In lakes, SPM exerts strong absorption and backscattering, ultimately affects water leaving signals that can be detected by satellite sensors. Simple regression models based on specific band or hand ratios have been widely used for SPM estimate in the past with moderate accuracy. There are still rooms for model accuracy improvements, and machine learning models may solve the non-linear relationships between spectral variable and SPM in waters. We assembled more than 16,400 in situ measured SPM in lakes from six continents (excluding the Antarctica continent), of which 9640 samples were matched with Landsat overpasses within ±7 days. Seven machine learning algorithms and two simple regression methods (linear and partial least squares models) were used to estimate SPM in lakes and the performance were compared. To overcome the problem of imbalance datasets in regression, a Synthetic Minority Over-Sampling technique for regression with Gaussian Noise (SMOGN) was adopted in this study. Through comparison, we found that gradient boosting decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost) models demonstrated good spatiotemporal transferability with SMOGN processed dataset, and has potential to map SPM at different year with good quality of Landsat land surface reflectance images. In all the tested modeling approaches, the GBDT model has accurate calibration (n = 6428, R2 = 0.95, MAPE = 29.8%) from SPM collected in 2235 lakes across the world, and the validation (n = 3214, R2 = 0.84, MAPE = 38.8%) also exhibited stable performance. Further, the good performances were also exhibited by RF model with calibration (R2 = 0.93) and validation (R2 = 0.86, MAPE = 24.2%) datasets. We applied GBDT and RF models to map SPM of typical lakes, and satisfactory result was obtained. In addition, the GBDT model was evaluated by historical SPM measurements coincident with different Landsat sensors (L5-TM, L7-ETM+, and L8-OLI), thus the model has the potential to map SPM of lakes for monitoring temporal variations, and tracks lake water SPM dynamics in approximately the past four decades (1984–2021) since Landsat-5/TM was launched in 1984.