The Impacts of Bio-Based and Synthetic Hydrogels on Soil Hydraulic Properties: A Review.

Soil hydraulic properties are important for the movement and distribution of water in agricultural soils. The ability of plants to easily extract water from soil can be limited by the texture and structure of the soil, and types of soil amendments applied to the soil. Superabsorbent polymers (hydrogels) have been researched as potential soil amendments that could help improve soil hydraulic properties and make water more available to crops, especially in their critical growing stages. However, a lack of a comprehensive literature review on the impacts of hydrogels on soil hydraulic properties makes it difficult to recommend specific types of hydrogels that positively impact soil hydraulic properties. In addition, findings from previous research suggest contrasting effects of hydrogels on soil hydraulic properties. This review surveys the published literature from 2000 to 2020 and: (i) synthesizes the impacts of bio-based and synthetic hydrogels on soil hydraulic properties (i.e., water retention, soil hydraulic conductivity, soil water infiltration, and evaporation); (ii) critically discusses the link between the source of the bio-based and synthetic hydrogels and their impacts as soil amendments; and (iii) identifies potential research directions. Both synthetic and bio-based hydrogels increased water retention in soil compared to unamended soil with decreasing soil water pressure head. The application of bio-based and synthetic hydrogels both decreased saturated hydraulic conductivity, reduced infiltration, and decreased soil evaporation. Hybrid hydrogels (i.e., a blend of bio-based and synthetic backbone materials) may be needed to prolong the benefit of repeated water absorption in soil for the duration of the crop growing season.

Quantifying the interaction of water and radiation use efficiency under plastic film mulch in winter wheat.

Local natural resources, (e.g., precipitation, solar radiation) are important for developing environmentally and scientifically sound management practices in dryland agroecosystem. Maximizing water use efficiency (WUE) in dryland farming systems remains a challenge. The objectives of this study were to assessing the robustness of radiation use efficiency (RUE) during different periods and investigate the interaction between RUE and WUE from water loss pattern and canopy development during wheat growth under different agricultural practices (non-mulched control, CK; transparent film mulching, TF; and black film mulching, BF) from 2013 to 2016 on the Loess Plateau, Northwest China. Results showed that RUE was mainly improved during post-anthesis under PM treatments. PM treatments contributed to elevated canopy photosynthesis and a delayed RUE peak during the reproductive period. Due to the increased spike number and ratio of plant transpiration to soil evaporation, TF and BF treatments had relatively stable photosynthetic activity relative to the CK treatment even those during dry periods. Initially, no relationship was found between WUE and RUE under the CK treatment. On the other hand, RUE and WUE were positively related in TF and BF treatments following a power function. RUE values increased with WUE rapidly to stabilize at a plateau value of 5.5 g MJ-1 under TF and BF treatments, and thus, the wheat WUE had a higher improvement potential than RUE as it did not have an apparent plateau value. PM treatments enhanced the wheat production by taking full advantage of local solar radiation and precipitation (improving RUE and WUE). This higher use efficiency of resources produced more photoassimilates for wheat than that under the CK management, increased source size (LAI) and sink size (spike number) during wheat growth seasons, and thus increased the final grain yield.

Plant science decadal vision 2020-2030: Reimagining the potential of plants for a healthy and sustainable future.

Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020-2030 frames our ability to perform vital and far-reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1-4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant-based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid-response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non-invasive imaging, sensors, and plug-and-play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence-assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

Classifying Reflectance Targets under Ambient Light Conditions using Passive Spectral Measurements.

Collecting remotely sensed spectral data under varying ambient light conditions is challenging. The objective of this study was to test the ability to classify grayscale targets observed by portable spectrometers under varying ambient light conditions. Two sets of spectrometers covering ultraviolet (UV), visible (VIS), and near-infrared (NIR) wavelengths were instrumented using an embedded computer. One set was uncalibrated and used to measure the raw intensity of light reflected from a target. The other set was calibrated and used to measure downwelling irradiance. Three ambient-light compensation methods that successively built upon each other were investigated. The default method used a variable integration time that was determined based on a previous measurement to maximize intensity of the spectral signature (M1). The next method divided the spectral signature by the integration time to normalize the spectrum and reveal relative differences in ambient light intensity (M2). The third method divided the normalized spectrum by the ambient light spectrum on a wavelength basis (M3). Spectral data were classified using a two-step process. First, raw spectral data were preprocessed using a partial least squares (PLS) regression method to compress highly correlated wavelengths and to avoid overfitting. Next, an ensemble of machine learning algorithms was trained, validated, and tested to determine the overall classification accuracy of each algorithm. Results showed that simply maximizing sensitivity led to the best prediction accuracy when classifying known targets. Average prediction accuracy across all spectrometers and compensation methods exceeded 93%.

Ten-year regional monitoring of soil-rice grain contamination by heavy metals with implications for target remediation and food safety.

Farmland soil heavy metal contamination could pose potential risks to ecosystems, food safety and human health ultimately. Regional researches on the long-term monitoring of heavy metals in a soil-rice grain system, changed with environmental policy adjustment, have been hindered by limited detailed data. In this study, we collected 169 paired paddy rice grain and corresponding soil samples from a former intensive electronic-waste dismantling region to survey the current status of heavy metal contamination, and to reveal the temporal trends over the past decade based on the previous data obtained in 2006 and 2011. Moderate contaminations of Cd, Cu, Zn and Ni were observed in soil currently. Furthermore, 20.7% of rice grain samples exceeded the Cd threshold value. Cd, Cu, Zn and Pb shared the similar spatial distribution pattern with higher concentrations in northwest, which were contrary to Cr, Ni and As. Risk assessment indicated that much attention is required for the carcinogenic risk of Cr, Cd and As and non-carcinogen risk of Cr. Combining the spatial distribution of heavy metals in soil and rice grains, and the potential ecological risks, with the human health risks, the middle-west rice paddies were identified and proposed as priority areas. Percentage of soil Pb, Cd and Zn decreased in most area and slightly increased in northwest and east. Cu decreased in southwest and increased in central part, while Ni slightly increased in the whole region between 2006 and 2016. With the scrutiny of strict environmental policy, Cd still remained relatively constant levels in soil and rice grains during the last decade, which confirmed that the heavy metals were persisted over the long duration. Target sustainable and ongoing green remediation methods should be adopted urgently in specific area to guarantee food safety and human health for local residents.

Spatial variability of soil carbon stock in the Urucu river basin, Central Amazon-Brazil.

The Amazon Forest plays a major role in C sequestration and release. However, few regional estimates of soil organic carbon (SOC) stock in this ecoregion exist. One of the barriers to improve SOC estimates is the lack of recent soil data at high spatial resolution, which hampers the application of new methods for mapping SOC stock. The aims of this work were: (i) to quantify SOC stock under undisturbed vegetation for the 0-30 and the 0-100 cm under Amazon Forest; (ii) to correlate the SOC stock with soil mapping units and relief attributes and (iii) to evaluate three geostatistical techniques to generate maps of SOC stock (ordinary, isotopic and heterotopic cokriging). The study site is located in the Central region of Amazon State, Brazil. The soil survey covered the study site that has an area of 80 km(2) and resulted in a 1:10,000 soil map. It consisted of 315 field observations (96 complete soil profiles and 219 boreholes). SOC stock was calculated by summing C stocks by horizon, determined as a product of BD, SOC and the horizon thickness. For each one of the 315 soil observations, relief attributes were derived from a topographic map to understand SOC dynamics. The SOC stocks across 30 and 100 cm soil depth were 3.28 and 7.32 kg C m(-2), respectively, which is, 34 and 16%, lower than other studies. The SOC stock is higher in soils developed in relief forms exhibiting well-drained soils, which are covered by Upland Dense Tropical Rainforest. Only SOC stock in the upper 100 cm exhibited spatial dependence allowing the generation of spatial variability maps based on spatial (co)-regionalization. The CTI was inversely correlated with SOC stock and was the only auxiliary variable feasible to be used in cokriging interpolation. The heterotopic cokriging presented the best performance for mapping SOC stock.