Search Results (317)

As talk grows about billions or even trillions of dollars being directed toward potential “Net Zero” activities, it is imperative that the chemistry inherent in or driving those actions make scientific sense. The challenge is to close the mass and energy balances to the carbon and oxygen cycles in the Earth’s atmosphere and oceans. Several areas of climate science have been identified that chemists can investigate through methods that do not require a supercomputer or a climate model for investigation, most notably the following: (1) The carbon cycle, which still needs to be balanced, as many known streams, such as carbon to landfills, carbon in human-enhanced sewage and land runoff streams, and carbon stored in homes and other material, do not seem to have been accounted for in carbon balances used by the IPCC. (2) Ocean chemistry and balances are required to explain the causes of regional and local-scale salinity, pH, and anoxic conditions vs. global changes. For example, local anoxic conditions are known to be impacted by changes in nutrient discharges to oceans, while large-scale human diversions of fresh water streams for irrigation, power, and industrial cooling must have regional impacts on oceanic salinity and pH. (3) Carbon Capture and Storage (CCS) schemes, if adopted on the large scales being proposed (100s to 1000s of Gt net injection by 2100), should impact the composition of the atmosphere by reducing free oxygen, adding more water from combustion, and displacing saline water from subsurface aquifers. Data indicate that atmospheric oxygen is currently dropping at about twice the rate of CO₂ concentrations increasing, which is consistent with combustion chemistry with 1.5 to 2 molecules of oxygen being converted through combustion to 1 molecule of CO₂ and 1 to 2 molecules of H₂O, with reverse reactions occurring as a result of oxygenic photosynthesis by increased plant growth. The CCS schemes will sabotage these reverse reactions of oxygenic photosynthesis by permanently sequestering the oxygen atoms in each CO₂ molecule. Full article

The advancement of digital agriculture combined with computational tools and Unmanned Aerial Vehicles (UAVs) has opened the way to large-scale data collection for the calculation of vegetation indices (VIs). These vegetation indexes (VIs) are useful for agricultural monitoring, as they highlight the inherent characteristics of vegetation and optimize the spatial and temporal evaluation of different crops. The experiment tested three coffee genotypes (Catuaí 62, E237 and Iapar 59) under five water regimes: (1) FI 100 (year-round irrigation with 100% replacement of evapotranspiration), (2) FI 50 (year-round irrigation with 50% evapotranspiration replacement), (3) WD 100 (no irrigation from June to September (dry season) and, thereafter, 100% evapotranspiration replacement), (4) WD 50 (no irrigation from June to September (water stress) and, thereafter, 50% evapotranspiration replacement) and (5) rainfed (no irrigation during the year). The irrigated treatments were watered with irrigation and precipitation. Most indices were highest in response to full irrigation (FI 100). The values of the NDVI ranged from 0.87 to 0.58 and the SAVI from 0.65 to 0.38, and the values of these indices were lowest for genotype E237 in the rainfed areas. The indices NDVI, OSAVI, MCARI, NDRE and GDVI were positively correlated very strongly with photosynthesis (A) and strongly with transpiration (E) of the coffee trees. On the other hand, temperature-based indices, such as canopy temperature and the TCARI index correlated negatively with A, E and stomatal conductance (gs). Under full irrigation, the tested genotypes did not differ between the years of evaluation. Overall, the index values of Iapar 59 exceeded those of the other genotypes. The use of VIs to evaluate coffee tree performance under different water managements proved efficient in discriminating the best genotypes and optimal water conditions for each genotype. Given the economic importance of coffee as a crop and its susceptibility to extreme events such as drought, this study provides insights that facilitate the optimization of productivity and resilience of plantations under variable climatic conditions. Full article

The integration of remote sensing technology and machine learning algorithms represents a new research direction for the rapid and large-scale detection of water stress in modern agricultural crops. However, in solving practical agricultural problems, single machine learning algorithms cannot fully explore the potential information within the data, lacking stability and accuracy. Stacking ensemble learning (SEL) can combine the advantages of multiple single machine learning algorithms to construct more stable predictive models. In this study, threshold values of stomatal conductance (g_s) under different soil water stress indices (SWSIs) were proposed to assist managers in irrigation scheduling. In the present study, six irrigation treatments were established for winter wheat to simulate various soil moisture supply conditions. During the critical growth stages, g_s was measured and the SWSI was calculated. A spectral camera mounted on an unmanned aerial vehicle (UAV) captured reflectance images in five bands, from which vegetation indices and texture information were extracted. The results indicated that g_s at different growth stages of winter wheat was sensitive to soil moisture supply conditions. The correlation between the g_s value and SWSI value was high (R² > 0.79). Therefore, the g_s value threshold can reflect the current soil water stress level. Compared with individual machine learning models, the SEL model exhibited higher prediction accuracy, with R² increasing by 6.67–17.14%. Using a reserved test set, the SEL model demonstrated excellent performance in various evaluation metrics across different growth stages (R²: 0.69–0.87, RMSE: 0.04–0.08 mol m⁻² s⁻¹; NRMSE: 12.3–23.6%, MAE: 0.03–0.06 mol m⁻² s⁻¹) and exhibited excellent stability and accuracy. This research can play a significant role in achieving large-scale monitoring of crop growth status through UAV, enabling the real-time capture of winter wheat water deficit changes, and providing technical support for precision irrigation. Full article

Large-scale land reclamation has become common in northwestern China; however, low soil fertility and poor soil water-holding capacity limit agricultural production on these reclaimed lands, requiring increased fertilizer and irrigation inputs. Biochar, produced from agricultural waste, has shown potential in improving soil quality and water-holding capacity. In this two-year field study (2021 and 2022), we investigated the effects of biochar produced from maize straw on soil properties and grain yield of foxtail millet grown on newly reclaimed land. Three biochar treatments (3000, 4500, and 6000 kg ha⁻¹) were compared to a control (CK) with no biochar application. Biochar application resulted in increased soil organic matter, total phosphorus, total nitrogen, soil enzyme activity, and soil organic acid content. It also significantly decreased soil pH and bulk density. Compared with the CK, biochar increased available nitrogen from 29.7% to 108% in 2021 and 37.0% to 88.4% in 2022. Similarly, biochar increased available phosphorus from 64.7% to 143% in 2021 and 41.9% to 96.5% in 2022. Grain yields ranged from 3092 to 4753 kg ha⁻¹. Biochar treatments increased grain yield compared to the CK, ranging from 12.2% to 24.6% in 2021 and 27.1% to 53.7% in 2022. Correlation analysis revealed that soil pH was negatively related to soil oxalic acid content, phosphorus content, and sucrase activity. Available nitrogen and phosphorus contents were negatively related to soil bulk density and positively related to catalase activity. Soil water content was negatively correlated with soil bulk density and positively correlated with organic matter. In conclusion, biochar improved the rhizosphere soil pH and the effectiveness of soil fertility in the newly reclaimed soil, resulting in an enhanced grain yield of foxtail millet. Full article

Here we report on preliminary efforts to assess the potential to use reclaimed water from municipal wastewater treatment plants for irrigation of vineyards in Napa Valley, California, USA and Valle de Guadalupe, Baja California, Mexico. Vineyards in Napa Valley use a range of source waters including 70 L/s of reclaimed water during the summertime irrigation season. Reclaimed water is secondary effluent that undergoes filtration and disinfection and meets stringent total coliform (<240 MPN/100 mL) and turbidity (10 NTU) requirements. Vineyards in Valle de Guadalupe currently use regional groundwater supplies of marginal quality, and there is interest in expanding source waters to include reclaimed water from nearby Ensenada or the more remote Tijuana. Valle de Guadalupe is drier than Napa Valley and has ongoing salinity management challenges, making the region more sensitive to using reclaimed water for irrigation. Several social and economic factors facilitated the implementation of reclaimed water reuse in Napa Valley for vineyard irrigation, including (1) formation of an assessment district by interested growers to help finance pipeline construction, (2) a long-term reclaimed water vineyard irrigation study by agricultural experts that confirmed the reclaimed water was safe, and (3) a well-defined and relatively low unit cost of reclaimed water. In Valle de Guadalupe, the federal government has approved a project to transport 1000 L/s of reclaimed water over 100 km from Tijuana to Valle de Guadalupe. Questions remain including financing of the project, reclaimed water quality, grower interest in using reclaimed water, and community concerns for such a large-scale program. In considering reclaimed water reuse in vineyards, a key issue is implementation of long-term studies showing that reclaimed water is effectively treated and is safe for irrigation, especially from the standpoint of salt content. In addition, the cost of reclaimed water needs to be comparable with traditional water sources. Finally, in addition to assessing economic constraints, social constraints and water user concerns should be comprehensively addressed in the context of a regional integrated water management framework. Full article

The expansion of cropland in tropical regions has significantly accelerated in recent decades, triggering an escalation in water demand and changing the total water loss to the atmosphere (evapotranspiration). Additionally, the increase in areas dedicated to agriculture in tropical climates coincides with an increased frequency of drought events, leading to a series of conflicts among water users. However, detailed studies on the impacts of changes in water use due to agriculture expansion, including irrigation, are still lacking. Furthermore, the higher presence of clouds in tropical environments poses challenges for the availability of high-resolution data for vegetation monitoring via satellite images. This study aims to analyze 37 years of agricultural expansion using the Landsat collection and a satellite-based model (geeSEBAL) to assess changes in evapotranspiration resulting from cropland expansion in tropical climates, focusing on the São Marcos River Basin in Brazil. It also used a methodology for estimating daily evapotranspiration on days without satellite images. The results showed a 34% increase in evapotranspiration from rainfed areas, mainly driven by soybean cultivation. In addition, irrigated areas increased their water use, despite not significantly changing water use at the basin scale. Conversely, natural vegetation areas decreased their evapotranspiration rates by 22%, suggesting possible further implications with advancing changes in land use and land cover. Thus, this study underscores the importance of using satellite-based evapotranspiration estimates to enhance our understanding of water use across different land use types and scales, thereby improving water management strategies on a large scale. Full article

While atmospheric warming intensifies the global water cycle, regionalised effects of climate change on water loss, irrigation supply, and food security are highly variable. Here, we elucidate the impacts of the climate crisis on irrigation water availability and cropping area in Nepal’s largest irrigation scheme, the Sunsari Morang Irrigation Scheme (SMIS), by accounting for the hydraulic capacity of existing canal systems, and potential changes realised under future climates. To capture variability implicit in climate change projections, we invoke multiple Representative Concentration Pathways (RCPs; 4.5 and 8.5) across three time horizons (2016–2045, 2036–2065, and 2071–2100). We reveal that although climate change increases water availability to agriculture from December through March, the designed discharge of 60 m³/s would not be available in February-March for both RCPs under all three time horizons. Weed growth, silt deposition, and poor maintenance have reduced the current canal capacity from the design capacity of 60 m³/s to 53 m³/s up to 10.7 km from the canal intake (representing a 12% reduction in the discharge capacity of the canal). Canal flow is further reduced to 35 m³/s at 13.8 km from canal intake, representing a 27% reduction in flow capacity relative to the original design standards. Based on climate projections, and assuming ceteris paribus irrigation infrastructure, total wheat cropping area could increase by 12–19%, 23–27%, and 12–35% by 2016–2045, 2036–2065, and 2071–2100, respectively, due to increased water availability borne by the changing climate. The case for further investment in irrigation infrastructure via water diversion, or installation of efficient pumps at irrigation canal intakes is compelling. Such investment would catalyse a step-change in the agricultural economy that is urgently needed to sustain the Nepalese economy, and thus evoke beneficial cascading implications for global food security. Full article

Nature-Based Solutions (NBSs) are decentralised and planted system elements with multiple benefits, requiring sufficient irrigation during dry weather periods to ensure plant health. In this work, the effects of the large-scale implementation of NBSs in the city centre of Klagenfurt from a water supply perspective are investigated, combining hydraulic analysis with water resource availability. As the large-scale implementation of NBSs in public squares shows, a coordinated NBS implementation strategy is required to ensure compatibility with the city’s water resources and infrastructure. This also emphasises the importance of alternative water sources for sustainable operations. Full article

Climate change and variability are increasingly affecting agriculture and livelihoods in developing countries, with India being particularly vulnerable. Drought is one of the major climatic constraints impacting large parts of the world. We examined the effects of drought on crop productivity, evaluated the effectiveness of technologies in mitigating these impacts and quantified the resilience gained due to technology adoption. Resilience score and resilience gain are the two indicators used to quantify resilience. The study utilized data gathered from two villages situated in Karnataka, southern India, which have implemented the National Innovations in Climate Resilient Agriculture (NICRA) program, along with data from one control village. Drought has significantly impacted the yields, and the extent of reduction ranged from 23 to 62% compared to the normal year. Adoption of climate-resilient technologies, including improved varieties, water management and livestock practices proved beneficial in increasing yield and income during drought years. The resilience score of various technologies ranged from 71 to 122%, indicating that the technologies had realized an increase in yields in the drought year in comparison to the normal year. The extent of resilience gain ranged from 7 to 68%, indicating that the adoption of technologies contributed to the yield advantage over the farmers’ practice during drought. Water harvesting and critical irrigation have the highest resilience scores and gains, and in situ moisture conservation practices such as trench cum bunding (TCB) have comparable resilience scores and gains. The diversification of enterprises at the farm has a higher resilience score and gain. There is a need to identify climate-resilient technologies that can achieve higher resilience, as the solutions are context-specific. Further, promising technologies need to be scaled by adopting multiple approaches and by creating an enabling environment so as to increase resilience in agricultural systems. Full article

Atmospheric elevated CO₂ concentration (e[CO₂]) decreases plant nitrogen (N) concentration while increasing water use efficiency (WUE), fertigation increases crop nutrition and WUE in crop; yet the interactive effects of e[CO₂] coupled with two N-fertigation levels during deficit irrigation on plant gas exchange, root morphology and WUE remain largely elusive. The objective of this study was to explore the physiological and growth responses of ambient [CO₂] (a[CO₂], 400 ppm) and e[CO₂] (800 ppm) tomato plant exposed to two N-fertigation regimes: (1) full irrigation during N-fertigation (FIN); (2) deficit irrigation during N-fertigation (DIN) under two N fertilizer levels (reduced N (N1, 0.5 g pot⁻¹) and adequate N (N2, 1.0 g pot⁻¹). The results indicated that e[CO₂] associated with DIN regime induced the lower N2 plant water use (7.28 L plant⁻¹), maintained leaf water potential (−5.07 MPa) and hydraulic conductivity (0.49 mol m⁻² s⁻¹ MPa⁻¹), greater tomato growth in terms of leaf area (7152.75 cm²), specific leaf area (223.61 cm² g⁻¹), stem and total dry matter (19.54 g and 55.48 g). Specific root length and specific root surface area were increased under N1 fertilization, and root tissue density was promoted in both e[CO₂] and DIN environments. Moreover, a smaller and denser leaf stomata (4.96 µm² and 5.37 mm⁻²) of N1 plant was obtained at e[CO₂] integrated with DIN strategy. Meanwhile, this combination would simultaneously reduce stomatal conductance (0.13 mol m⁻² s⁻¹) and transpiration rate (1.91 mmol m⁻² s⁻¹), enhance leaf ABA concentration (133.05 ng g⁻¹ FW), contributing to an improvement in WUE from stomatal to whole-plant scale under each N level, especially for applying N1 fertilization (125.95 µmol mol⁻¹, 8.41 µmol mmol⁻¹ and 7.15 g L⁻¹). These findings provide valuable information to optimize water and nitrogen fertilizer management and improve plant water use efficiency, responding to the potential resource-limited and CO₂-enriched scenario. Full article

This publication presents an innovative tower cultivation device designed to significantly increase vertical farming’s efficiency. The device divides the cultivation system into separate chambers. One division corresponds to the different growth phases of the plants, while another reflects the daily variation in conditions. Each chamber presents slightly different conditions and cultivation patterns from the others. For the early stages, crops are grown horizontally in trays; once they mature, they are transplanted into mobile cultivation towers. The closed circulation of ventilation and irrigation reduces water consumption by up to 95%. A unique separate day–night division optimises light, temperature, and humidity conditions, mimicking natural growth patterns. This approach not only saves water and energy but also improves cultivation in a three-dimensional space. The presented solution focuses on the often-overlooked aspects of cultivating in vertical farms and makes this method of growing much more cost-effective and feasible to implement on a large scale. Our comparative analysis with other vertical farming solutions is based on publicly available data and provides valuable insights, while acknowledging the potential limitations at play. Full article

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field. Full article

Based on the success of an irrigation project that utilized two subsurface dams as water sources on Miyako Island, ten additional subsurface dams have now been completed. The technologies that have made the giant subterranean dam possible are the integrated storage model for creating water utilization plans and the Soil Mixed Wall method for constructing cut-off walls. Although it might be tempting to assume that all subsurface dams in the Ryukyu limestone region were built under identical topographical and geological conditions, the reality is quite different. Each dam faced unique geological and construction challenges that engineers skillfully overcame during the building process. The purpose of this paper is to introduce information on the planning and construction technology of agricultural subsurface dams in the Ryukyu Arc, which has not been reported in English so far, and to clarify the characteristics of agricultural subsurface dams in the Ryukyu Arc. There is a strong correlation between the gross reservoir capacity and the active capacity of large-scale subsurface dams. Eleven percent of the construction cost was the cost of design and investigation. The water price is the same as or slightly higher than that of surface dams. Full article

As a vital pigment for photosynthesis in rice, chlorophyll content is closely correlated with growth status and photosynthetic capacity. The estimation of chlorophyll content allows for the monitoring of rice growth and facilitates precise management in the field, such as the application of fertilizers and irrigation. The advancement of hyperspectral remote sensing technology has made it possible to estimate chlorophyll content non-destructively, quickly, and effectively, offering technical support for managing and monitoring rice growth across wide areas. Although hyperspectral data have a fine spectral resolution, they also cause a large amount of information redundancy and noise. This study focuses on the issues of unstable input variables and the estimation model’s poor applicability to various periods when predicting rice chlorophyll content. By introducing the theory of harmonic analysis and the time-frequency conversion method, a deep neural network (DNN) model framework based on wavelet packet transform-first order differential-harmonic analysis (WPT-FD-HA) was proposed, which avoids the uncertainty in the calculation of spectral parameters. The accuracy of estimating rice chlorophyll content based on WPT-FD and WPT-FD-HA variables was compared at seedling, tillering, jointing, heading, grain filling, milk, and complete periods to evaluate the validity and generalizability of the suggested framework. The results demonstrated that all of the WPT-FD-HA models’ single-period validation accuracy had coefficients of determination (R²) values greater than 0.9 and RMSE values less than 1. The multi-period validation model had a root mean square error (RMSE) of 1.664 and an R² of 0.971. Even with independent data splitting validation, the multi-period model accuracy can still achieve R² = 0.95 and RMSE = 1.4. The WPT-FD-HA-based deep learning framework exhibited strong stability. The outcome of this study deserves to be used to monitor rice growth on a broad scale using hyperspectral data. Full article

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province. Full article