Minimum Soil Temperature Research Articles

Utilization of the Google Earth Engine for the evaluation of daily soil temperature derived from Global Land Data Assimilation System in two different depths over a semiarid region

AbstractThe Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers.

Risk assessment of Phlyctinus callosus for the EU.

Following a request from the European Commission, the EFSA Panel on Plant Health performed a quantitative risk assessment for the EU of Phlyctinus callosus (Coleoptera: Curculionidae), a polyphagous pest occurring in Australia, New Zealand and South Africa. The current risk assessment focused on potential pathways for entry, the climatic conditions allowing establishment, the expected spread capacity and the impact considering a time horizon of 10 years (2023-2032). The Panel identified the import of apples, cut flowers and table grapes as the most relevant entry pathways. Over the next 10 years, an annual median estimate of approximately 49.5 (90% certainty range, CR, ranging from 4.0 to 881.2) potential P. callosus founder populations are expected. When the probability of establishment is considered and climatic indicators are used to define the areas in the EU where establishment is possible, the model estimated a median of 1 founder population every 1.3 years (90% CR: 1 every 30.8 years to 23.3 per year) in the scenario where the areas are defined by the union of all the climatic indicators and 1 founder population every 11.9 years (90% CR: 1 every 256.6 years to 2.5 per year) in the scenario where establishment is possible only in the areas defined by the climatic indicator of minimum soil temperature. The estimated number of founder populations per year is mostly driven by the probability of establishment in the rural areas, infestation rate in table grapes and the probability of transfer to a suitable host in the rural area. The risk of entry for cut flowers and apples is substantially lower than the risk from the table grapes. If such founder populations were to establish, P. callosus is estimated to spread by natural dispersal and common agricultural practices at a rate of 15.5 m/year (90% CR 5.1-46.8 m/year) after a lag phase of 4.0 years (90% CR 1.3-8.7 years). The impact, expressed as percentage loss of the production directly attributable to P. callosus in the areas where establishment is possible and assuming farmers do not apply specific control measures was estimated at 0.5% (90% CR 0.01%-2.8%) for cut flowers/foliage, 5.2% (90% CR 2.2%-11.7%) for apples and 2% (90% CR 1.3%-5.2%) for table grapes. Options for risk reduction are discussed, but their effectiveness is not quantified.

Responses of phenology to preseason drought and soil temperature for different land cover types on the Mongolian Plateau

Drought and heat caused major disturbance in nature by interfering with plant phenology, and can also alter the vulnerability and resilience of terrestrial ecosystems. Existing research on the Mongolian Plateau has primarily focused on studying the response of the start (SOS) and end (EOS) of the growing season to drought and heat variations. However, there is still a lack of comprehensive understanding regarding the coupled effects of drought and heat on phenology across different land cover types. In this study, we retrieved SOS and EOS based on 34-year (1982–2015) normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). Results showed that grasslands and the Gobi-Desert show rapid advancement in SOS, and forests presented the slowest advancement in SOS, but SOS in croplands were delayed. EOS across four land cover types advanced, with the Gobi-Desert showed the highest rate of advancement and forests the lowest. Using the Palmer Drought Severity Index (PDSI) and soil temperature as the indicators of drought and thermal conditions, the responses of SOS and EOS to these two climate variables were evaluated. The advanced SOS driven by lower drought severity was detected in forests, grasslands, croplands and the Gobi-Desert. The dominant response of EOS to drought severity was positive in croplands, grasslands and forests, except for the Gobi-Desert, where drought severity had negative effects on EOS. Compared with the daily average soil temperature (STmean), the daily maximum soil temperature (STmax, daytime), and the daily minimum soil temperature (STmin, nighttime), the daily diurnal soil temperature range (DSTR, where DSTR = STmax − STmin) between night and day were the most suitable indicators for assessing the response of SOS and EOS to soil temperature. Strong negative correlation between SOS and the preseason DSTR was pronounced in all land cover types on the Mongolian Plateau. However, EOS was negatively correlated with the preseason DSTR only in the Gobi-Desert. Last but not least, normalized sensitivity assessments reveal that the negative impacts of DSTR on SOS and EOS were the main controlling factors on the Mongolian Plateau phenology, followed by the couple negative effects of drought severity and DSTR.

Vegetation responses to snow cover removal and rainfall reduction in subalpine grasslands: Insights from a 4-year field experiment in the Spanish Pyrenees

ABSTRACT Reduced precipitation as well as warming may result in less snow accumulation in seasonally snow-covered areas, leading to lower minimum soil temperatures and more frequent and severe soil frosts. Therefore, plant stress is increased not only by drought in warmer months, but also by increased exposure to frost in cooler periods. We conducted a 4-year field experiment to evaluate the effects of diminished snowpack accumulation (through snow removal) and rainfall reduction (through rain-out shelters) on aboveground plant productivity, diversity and species composition of two subalpine grassland plant communities from the central region of the Spanish Pyrenees. We found that the snow removal treatment decreased minimum soil temperature by 0.5°C. Plant diversity decreased by 16 percent, although this effect was only observed in one of the grasslands studied. Aboveground primary productivity seemed to be unaffected. In contrast, we found that the rainfall reduction treatment negatively affected aboveground productivity of leguminous forb species, yet no effect on plant diversity was observed. Both treatments were important drivers of changes in plant species composition. Overall, our results suggest that the resilience of subalpine grasslands to snow cover removal and rainfall reduction treatments may depend on the specific community composition and dominant plant groups.

An empirical and a dynamic-empirical model for the estimation of maize seedbed temperature

Soil temperature is the main factor in determining the germination of maize seeds and the emergence time of the crop. It controls the rate of phenological development while the meristem is underground, which is until the V6 (six leaf collar) stage of maize. The research performed by the authors aimed to model maize seedbed temperatures at sowing depth (soil temperature at 5 cm depth) during the sowing-emergence-early development period. The research is based on measurements in ploughed plots of the maize experiments at the Látókép Experiment Site of the University of Debrecen (Eastern Hungary) in two growing seasons of 2021–2022. Two types of empirical models were established, a multilinear regression model (M1) and a dynamic-empirical model (M2), where the daily increase and decrease of soil temperature are determined by multilinear regression. Candidates for input variables for both models were various, easily available daily meteorological parameters. M2 model performed better than M1 when applied to an independent database of 2022. This is particularly valid for the maximum and minimum soil temperatures. It was found that both M1 and M2 can be used to predict the soil temperature of the maize seedbed before shading by the plants. For daily mean temperature, M1 and M2 give a similarly good estimation, while the dynamic-empirical model has to be preferred for the maximum and minimum temperatures. M2, which is based on daily temperature, global radiation and wind speed data, predicts the daily mean (RMSE = 1.4 °C), maximum (RMSE = 2.2 °C), and minimum (RMSE = 1.6 °C) of seedbed temperature not worse than many earlier soil temperature models do, even hybrid or mechanistic ones with a large number of parameters.

Integrative indexes reveal the tolerance of winter wheat to different overwinter freezing injury.

Winter wheat (Triticum aestivum L.) is a crucial crop that guarantees food supply in the North China Plain (NCP). As the frequency of extreme cold events increases, it is necessary to explore the freezing resistance of different wheat varieties in order to clarify planting boundaries and help with risk assessment. In this study, 2-year controlled experiments were conducted to explore the effect of freezing temperatures (T air) and freezing durations on three winterness types. A set of indexes were used to characterize the subfreezing stress on wheat tiller, leaf, and final yield. Logistical regressions were used to quantify the temperature threshold for 10%, 30%, and 50% of freezing injury. The results showed that the lower temperature threshold of tiller (LT) varied from -9.6 to -15.9°C, -10.7 to -19.1°C and -11.4 to -21.2°C for LT10, LT30, and LT50, respectively. The difference between LT and yield loss (YL) indexes reduced with decreased winterness types and was -0.1 to 3.4°C, -0.7 to 2.1°C, and 0.3 to 0.9°C higher compared with YL thresholds for winterness, semi-winterness, and weak-winterness types, respectively. The average minimum soil temperature was 7.5, 4.8, and 4.2°C higher than T air for 1-, 2-, and 3-day treatment, respectively. Soil effective negative accumulated temperature hours (TSEh) ranged from 6.9 to 12.0, 48.4 to 6.9, and 84.7 to 106.9°C·h for 10%, 30%, and 50% tiller mortality, respectively. Freezing treatment with T air < -12, -9, and -8°C obviously decreased leaf Fv/Fm for the three varieties and Fv/Fm declined obviously after 5 days of recovery under field conditions. Our results provided multiple indexes for quantifying subfreezing damage in practical wheat production and could shed light on future risk assessment.

Compositional shifts of alpine plant communities across the high Andes

AbstractAimClimate change is transforming mountain summit plant communities worldwide, but we know little about such changes in the High Andes. Understanding large‐scale patterns of vegetation changes across the Andes, and the factors driving these changes, is fundamental to predicting the effects of global warming. We assessed trends in vegetation cover, species richness (SR) and community‐level thermal niches (CTN) and tested whether they are explained by summits' climatic conditions and soil temperature trends.LocationHigh Andes.Time periodBetween 2011/2012 and 2017/2019.Major taxa studiedVascular plants.MethodsUsing permanent vegetation plots placed on 45 mountain summits and soil temperature loggers situated along a ~6800 km N‐S gradient, we measured species and their relative percentage cover and estimated CTN in two surveys (intervals between 5 and 8 years). We then estimated the annual rate of changes for the three variables and used generalized linear models to assess their relationship with annual precipitation, the minimum air temperatures of each summit and rates of change in the locally recorded soil temperatures.ResultsOver time, there was an average loss of vegetation cover (mean = −0.26%/yr), and a gain in SR across summits (mean = 0.38 species m2/yr), but most summits had significant increases in SR and vegetation cover. Changes in SR were positively related to minimum air temperature and soil temperature rate of change. Most plant communities experienced shifts in their composition by including greater abundances of species with broader thermal niches and higher optima. However, the measured changes in soil temperature did not explain the observed changes in CTN.Main conclusionsHigh Andean vegetation is changing in cover and SR and is shifting towards species with wider thermal niche breadths. The weak relationship with soil temperature trends could have resulted from the short study period that only marginally captures changes in vegetation through time.

Thermal biology and overwintering behavior of the red sunflower seed weevil (Coleoptera: Curculionidae).

Most natural mortality of the red sunflower seed weevil, Smicronyx fulvus LeConte (Coleoptera: Curculionidae), occurs while larvae overwinter in the soil. To test the hypothesis that S. fulvus mortality is related to low temperatures, experiments were used to (i) evaluate the temperature at which larvae freeze (= supercooling point [SCP]), (ii) assess possible vertical movement between entry into the soil in fall and adult emergence in summer, and (iii) determine if realistic soil temperatures could explain patterns of overwintering mortality. Mean SCP for groups of S. fulvus larvae differed between years and months, but only ranged from -20.93 to -22.68 °C. Most overwintering larvae were found within 6 cm of the soil surface, but larvae appeared to move 1-2 cm deeper between pairs of successive sample dates (September to January, January to April). Significant larval mortality that occurred between January and April 2021 was tentatively attributed to a period in February where daily minimum soil temperatures ranged from -8 to -12 °C. When overwintering under control conditions (constant 4 °C) was interrupted with week-long exposure to -4, -8, or -12 °C in a cold bath, significant S. fulvus mortality was seen for temperatures at or below -8 °C. Combined results suggest that mortality of overwintering S. fulvus is likely caused by continuous exposure to low temperatures that may not be cold enough to freeze larvae. Additionally, the shallow overwintering by S. fulvus supports the idea that routine farm management, including tillage and herbicide incorporation, may help limit populations of this sunflower pest.

The habitat-suitability models of the European mole cricket (Gryllotalpa gryllotalpa) as information tool for conservation and pest management

The European mole cricket, Gryllotalpa gryllotalpa, feeds on a wide range of crops and can also damage plants with its burrowing activities. In suitable habitats (like those with damp, rich soils in flood plains), G. gryllotalpa numbers can increase to high levels. On the other hand, the abundance of G. gryllotalpa has dramatically decreased in north-western Europe partly due to direct eradication and excessive pest control. Using habitat suitability modelling, we identified areas suitable for G. gryllotalpa occurrence based on previous reports of its occurrence and based on environmental data. We limited our study area to regions where G. gryllotalpa is the only known Gryllotalpa species. The most important variables in our models were found to be relative air humidity and minimum soil temperature of the coldest month. We suggest that soil temperature is a limiting factor for European mole cricket occurrence in the Czech Republic because most areas in the country experience soil temperatures just below 0 °C, while most reports of G. gryllotalpa occurrence in Europe are from areas where the soil temperature does not drop below 0 °C. The models we have developed can provide information on possible occurrences of the mole cricket and thus improve the decision-making process both in the field of pest control and the conservation of this species.

Influence of wildfire on the rapidly changing features of patchy permafrost, Northeast China

AbstractAs an active ecological agent in the boreal forest, wildfires have many important impacts on the underlying permafrost. However, investigations of post‐fire changes in the features of ecosystem‐protected permafrost are extremely limited in the patchy permafrost regions, Northeast China. Additionally, the impacts of wildfires on patchy permafrost are complicated in this region. In this paper, based on field surveys and observations, permafrost features and their influencing factors were analyzed in the area burned in 2006. The results showed notably lower soil temperatures in shrub‐wetland (NWH1, unburned and NWH2, light burn) in comparison with those in the birch‐larch forest (NWH3, light burn and NWH4, moderate burn). Changes in shallow ground temperatures (0–4 m in depth) were significant. Mean annual soil temperatures (MAST) and annual maximum soil temperature at the light‐burn NWH2 site were 0.3–5.2°C higher than those of unburned NWH1, but annual minimum soil temperatures, 0.3–2.1°C lower. MASTs at the moderately burned NWH4 were 0.6–1.6°C higher than those at the light‐burn NWH3. The active layer thickness (ALT) was greater at burned sites, and; at the moderately burned site, only seasonal frost was found when 13 years after the burn. The contents of soil organic carbon and total nitrogen were the highest at the unburned NWH1 from 0.0 to 0.4 m in depth, followed by those at the lightly‐burned NWH2 and NWH3, and the lowest at the moderately‐burned NWH4. Climate warming, forest fires, and changing local factors (e.g., soil moisture contents, snow cover, soil texture and drainage conditions) worked together to increase ground temperature and ALT. These research results could provide important scientific basis for the protection of boreal forest and wetlands and the ecosystem‐protected permafrost under a warming climate.

ОСОБЕННОСТИ ЭКОЛОГИЧЕСКИХ ТРЕБОВАНИЙ ХЛОПЧАТНИКА К УСЛОВИЯМ ПРОИЗРАСТАНИЯ

Purpose: to determine the features of the ecological requirements of cotton to growth conditions. Discussion. To expand the area of cotton growing in Russia, it is necessary to solve the problems of acclimatization and zoning of cotton, since this crop has its own specific agro-requirements for growth conditions. The main factors influencing the development and productivity of a crop are temperature, moisture, light, soil, nutrient regime, and terrain. It has been determined that the minimum soil temperature at which cotton seeds germinate is 10–12 °C, the optimum air temperature for plant development is approximately 25–30 °C, but not higher than 36 °C, since cotton is depressed. It is drought tolerant but requires irrigation to increase yield. Cotton needs evenly diffused sunlight. The light regime is regulated by the seeding rate, the seeding method and the direction of rows. The best soils are loamy; heavy clay soils are suitable, but costly to improve water-physical properties; soils with a close occurrence of groundwater are of little use. Saline soils are suitable for chloride-sulfate salinization with a salt content of no more than 0.4 %, and for sodium sulfate – no more than 0.8 %. The content of exchangeable sodium in soil should not exceed 5 % of the total soil absorption complex. To form 1 ton of raw cotton, 50–60 kg of nitrogen, 10–15 kg of phosphorus, 50–60 kg of potassium and 50 kg of calcium are required. The flat surface is the most favorable for cotton cultivation. Conclusions. Knowledge of the ecological requirements for cotton will make it possible to assess the feasibility of cultivating this crop in a particular area.

Trends in soil frost formation in a warming maritime climate and the impacts on urban flood risk

The most severe urban flooding in cold maritime climates is due to the co-action of long-duration rainfall, snowmelt, and soil frost. Increasing winter air temperature due to climate change is projected to change the magnitude and frequency of rain-on-snow (RoS) events and increase the number of freeze–thaw cycles and midwinter snowmelt. While daily rainfall records are readily available, less is known about the infiltration and frost formation within urban soils. Thus, there is uncertainty on how warming winter conditions may affect urban flood risk and the climate resilience of cities. The aim of this study was, therefore, to assess soil frost formation in the past 70 years in the maritime city of Reykjavík, Iceland (64° N, 21° W), and its co-action with runoff generation, and the potential implications on urban flood risk. To that end, the daily thermal and hydraulic conditions of the soil were simulated using the Simultaneous Heat and Water (SHAW) model dating back to 1949, calibrated based on hourly observations from 2007. Model simulations indicated that the minimum soil temperature at 10 cm depth has been warming at a rate of 0.015 °C/year in the past seven decades. Climate warming is also noted in a steady decline in frost depth and the duration of soil frost each winter (p-value < 0.05). However, the freezing season has shortened so that the timing of maximum frost coincides more with the timing of maximum RoS and snowmelt events. Furthermore, RoS events during frost have the capacity to produce larger runoff volumes than rainfall or snowmelt alone based on the joint frequency analysis of winter events using the copula method. This, combined with increasing volume during RoS events, suggests that winter floods may intensify in the next decades, which urges re-thinking urban stormwater management in cold climates.

Effect of mulching materials on soil temperature and horticultural attributes in pear (Pyrus pyrifoila (Burm.) Nakai) under humid subtropical climate of Punjab

ABSTRACT- The effect of mulching materials on soil temperature and horticultural attributes in pear under subtropics of northwestern India was studied for years 2018 and 2019. Different mulches such as paddy straw mulch (PSM), white polythene mulch (WPM) and black polythene mulch (BPM) were applied underneath the canopies of ‘Patharnakh’ pear trees during second week of April. The weedy check (control) and manual weeding plots were kept to compare the relative effects of different mulching materials. The observations on soil temperature were noted at weekly interval while the quality parameters of pear fruit were recorded at harvest. Among the time intervals of soil temperatures, the higher soil temperature was observed with the WPM mulch followed by BPM while the minimum soil temperature was recorded with PSM. The WPM treatment showed soil warming effect from 16th to 28th standard meteorological week (SMW) during both the years of investigation. Different mulches significantly influenced the fruit yield and quality attributes of Patharnakh pear. The maximum fruit weight and fruit yield was observed in PSM, while minimum in control as weedy check. The internal quality of pear fruits in terms of total soluble solids was better in PSM and WPM as compared to fruits from control plants. It can be summarized that the readily available soil moisture and optimum soil temperature conditions during fruit development period improves fruit size particularly under PSM and WPM.&#x0D; Key words: Soil temperature, mulches, management practices, pear, fruit attributes.

Population Dynamics of Red Ant &lt;i&gt;Dorylus Orientalis&lt;/i&gt; in Potato Grown Under Flood Free/ Prone Conditions

Experiments were carried out in the farmer’s field at Jorhat during 2015-16 and 2016-17 to study the population dynamics of red ant Dorylus orientalis Westwood in potato crop. The incidence of D. orientalis was observed both under flood free/ and prone conditions in three villages each, during potato growing season. These data were correlated with weather factors. The effect on population buildup was studied with observations at fortnightly interval starting from 1st fortnight of October to 2nd fortnight of February through fixed plot surveys. The observations revealed that maximum incidence (36.5, 33.2 and 30.1; 35.2, 32.5 and 29.8/ pit) was observed in Charaibahi, Karangia and Hokai Khangia villages, respectively during the 1st fortnight of January. In contrast, in the flood prone villages, incidence was observed to be negligible. Correlation coefficients of incidence with weather factors revealed that minimum temperature, soil temperature (morning and evening) and evaporation rate show a significantly negative correlation; whereas relative humidity (RH) (morning and evening) exhibited significant positive correlation in flood free villages. In flood prone villages minimum temperature as well as morning soil temperature showed significant negative correlation (2015-16); in contrast, in flood prone villages, RH (morning and evening) showed significant positive correlation (2016-17).

Three-Dimensional Dynamic Variations of Ground/Air Surface Temperatures and Their Correlation with Large-Scale Circulation Indexes in Southwest China (1980–2019)

Air/soil temperatures play important roles in land–atmosphere interactions. The three-dimensional (temporal, spatial, and vertical) variations of maximum, mean, and minimum ground soil temperature at 0 cm (GSTx, GSTm, and GSTn, respectively), surface air temperature at 2 m (SATx, SATm, and SATn, respectively), and soil–air temperature difference (SATDx, SATDm, and SATDn, respectively) and their potential linkages with large-scale indexes in Southwest China during 1980–2019 were analyzed. Variations of GST and SAT at the majority of stations (pixels) exhibited significant (p ≤ 0.05) warming, albeit at different rates; consequently, SATD exhibited different variation. Moreover, the period of GST, SAT, and SATD was similar in intra-annual and interannual oscillation but was different in interdecadal oscillation. The variation rate of GST, SAT, and SATD exhibited significant (p ≤ 0.05) correlation with elevation, but with different variation gradient. Notably, asymmetric variation of SATDx (downward trend) and of SATDn (upward trend) with elevation was found at elevations &gt;3 km. Wavelet coherence showed that the Atlantic Multidecadal Oscillation is the dominant factor affecting GST and SAT, whereas the Pacific Decadal Oscillation and the North Atlantic Oscillation make the greatest contributions to SATD. It was found that GST, SAT, and SATD exhibit different variations under the effects of global warming, the driving mechanism of which requires further study.

Nitrogen management and supplemental irrigation affected greenhouse gas emissions, yield and nutritional quality of fodder maize in an arid region

Deficit and erratic precipitation in arid regions and imbalance nitrogen (N) fertilization can result in lower yield and nutritional quality of fodder maize. The objectives of the experiment were to investigate the effect of N (urea 46% N) rates i.e., 225 (N1), 300 (N2) and 375 kg N ha–1 (N3) under 600 (W1) and 900 mm ha–1 (W2) supplemental irrigation levels on the greenhouse gas (GHG) emissions, yield and nutritional quality of fodder maize. The treatments combination comprised of N1W1, N2W1, N3W1, N1W2, N2W2 and N3W2. N fertilization and supplemental irrigation levels significantly affected soil moisture content (SMC) and soil temperature (ST), whereas maximum SMC and minimum ST were recorded in N3W2. Increasing N rate decreased soil ammonium nitrogen content (NH4+-N) and increased nitrate nitrogen content (NO3–-N) and maximum NH4+-N was recorded in N1W1 and maximum NO3–-N in N3W1. Methane (CH4) uptake was higher in W1 compared with the W2, and maximum CH4 uptake was recorded in N3W1 followed by N3W2. Nitrous oxide (N2O) and carbon dioxide (CO2) emissions were higher in W1 compared with the W2, and maximum N2O and CO2 emissions were recorded in N3W1. Cumulative emission of N2O and CO2, CH4 uptake, global warming potential (GWP), and greenhouse gas intensity (GHGI) were higher in W1 compared with W2 and their maximum values were recorded in N3W1. Treatment N3W2 significantly improved the forage yield and nutritional quality of fodder maize by improving the crude protein content and ether extract content, while reducing neutral detergent and acid detergent fibers contents. In conclusion, treatment N3W2 improved SMC, forage yield, grain yield, and nutritional quality of fodder maize as well as reduced GHG emissions, GWP and GHGI in an arid region.

Assessment of Water Stress in Vineyards Using On-The-Go Hyperspectral Imaging and Machine Learning Algorithms

<b>Highlights</b> <list list-type=bullet><list-item> Many vineyards are located in arid and semi-arid climatic conditions with dry summers. </list-item><list-item> Optimal water supply is crucial to avoid any yield or quality loss due to under-irrigation or over-irrigation, respectively. </list-item><list-item> Hyperspectral images, weather data, and machine learning models were used to classify water stress into three categories. </list-item><list-item> Optimal random forest classifier (RFC) and artificial neural network (ANN) models had accuracies of 73% and 70%, respectively. </list-item></list> <b>Abstract</b>. Efficient use of scarce water resources for optimum crop yield and quality is a major concern in dry lands. Traditionally, in-situ methods such as measurements of leaf water potential (ΨL) or soil moisture have been used for estimating plant water stress in vineyards. However, these methods are time, labor, and cost-intensive, and limited to point measurements of water stress that do not offer much spatial resolution. In this study, hyperspectral images acquired from a ground-based utility vehicle were analyzed to evaluate the applicability of hyperspectral data in classifying plant water stress measured as ΨL into three classes: no to mild water stress (ΨL > −0.8 MPa), moderate water stress (-0.8 MPa ≤ ΨL ≤ -1.2 MPa) and severe water stress (ΨL < -1.2 MPa). A field trial was carried out in an experimental vineyard in arid southeastern Washington, USA. Grapevines were subjected to either full irrigation (FI) or regulated deficit irrigation (RDI). Several vegetative indices (VIs) derived from hyperspectral reflectance information were assessed to estimate the level of water stress. Linear relationships between VIs and ΨL, and relative variable importance based on Gini impurity in different test models were evaluated to select optimal VIs for vine water stress classification. Green Normalized Difference Vegetation Index (GNDVI), Photochemical Reflectance Index (PRI), and Anthocyanin Index (ANT) showed consistently good trends in the data obtained over two growing seasons and were selected as the optimal VIs. In addition, air relative humidity, evapotranspiration, minimum soil temperature, and solar radiation demonstrated high significance in model development and were included in the final optimized random forest classifier (RFC) and artificial neural network (ANN) models. The RFC model had an accuracy of 73% while the ANN model had an accuracy of 70% in classifying plant water condition into three classes. Both models demonstrated good classification abilities and can be applied to analyze vine water stress and developing decision support tools for precision irrigation in vineyards.

Evidences of soil warming from long-term trends (1951\u20132018) in North Rhine-Westphalia, Germany

Soil temperature (ST) is an important property of soils and driver of below ground biogeochemical processes. Global change is responsible that besides variable meteorological conditions, climate-driven shifts in ST are observed throughout the world. In this study, we examined long-term records in ST by a trend decomposition procedure from eleven stations in western Germany starting from earliest in 1951 until 2018. Concomitantly to ST data from multiple depths (5, 10, 20, 50, and 100 cm), various meteorological variables were measured and included in the multivariate statistical analysis to explain spatiotemporal trends in soil warming. A significant positive increase in temperature was more pronounced for ST (1.76 ± 0.59 °C) compared with air temperature (AT; 1.35 ± 0.35 °C) among all study sites. Air temperature was the best explanatory variable to explain trends in soil warming by an average 0.29 ± 0.21 °C per decade and the trend peaked during the period from 1991–2000. Especially, the summer months (June to August) contributed most to the soil warming effect, whereby the increase in maximum ST (STmax) was nearby fivefold with 4.89 °C compared with an increase of minimum ST (STmin) of 1.02 °C. This widening between STmax and STmin fostered enhanced diurnal ST fluctuations at ten out of eleven stations. Subsoil warming up to + 2.3 °C in 100-cm depth is critical in many ways for ecosystem behavior, e.g., by enhanced mineral weathering or organic carbon decomposition rates. Thus, spatiotemporal patterns of soil warming need to be evaluated by trend decomposition procedures under a changing climate.Graphical abstract

Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation

Soil moisture content (SMC) prediction can contribute to diverse geo-science engineering applications such as plantation, crops production, and several irrigation activities. Although, there have been several methodologies introduced for the SMS estimation, methods are still associated with challenges and limitations (e.g., time-consuming, high cost, and the need for large data sets). In the current research, the motivation was inspired to develop new data-intelligence models that are capable to estimate SMC accurately and reliably. Several hybridized adaptive neuro fuzzy inference system (NF) models were developed. The feasibility of four meta-heuristic algorithms including the gray wolf optimization (GWO), Bee algorithm (BA), Firefly algorithm (FA), and imperialistic competitive algorithm (ICA), were used to improve the predictability performance of the NF model. The performances of the hybrid models were tested at two different climate characteristics (i.e., humid and arid) and four different soil depths (5, 10, 20, and 50 cm). The dataset used in this study were consisted of ten years with daily scale. Practical inputs predictors for the prediction matrix were identified using the mutual information method (MI) and correlation coefficient, which included maximum and minimum temperature, solar radiation, and soil temperature. K-Fold technique and various indices were used to evaluate hybrid models. Despite the promising results of all developed models, the NF-FA is superior to the others in both climates. The average results of all depths (in humid climate) using correlation coefficient (r), mean absolute percentage error (MAPE), and scattering index (SI) were 0.924, 6.089%, and 0.074, respectively. While in the arid climate, NF-FA model attained 0.903, 14.439%, and 0.176 in r, MAPE, and SI, respectively. The evaluation of different depths and climates showed that NF-FA yielded the best accuracy within humid climates and depths of 20 and 10 cm had the lowest estimation error in humid and arid climates. This study introduced the FA algorithm as an efficient tool in NF development to estimate SMC by readily available inputs without knowing soil physical parameters.

The effects of soil temperature from soil mulching and harvest age on phenol, flavonoid and antioxidant contents of Java tea (Orthosiphon aristatus B.)

BackgroundThe environmental conditions resulted by the agronomic management practices may govern the secondary metabolite contents of medicinal plants, including Java tea (Orthosiphon aristatus B). Abiotic factors such as temperatures have been known to determine the secondary metabolite contents of Java tea. This study aimed at evaluating the effects of soil temperature resulting from soil mulching and harvest age on total phenol, flavonoid and antioxidant contents of Java tea.MethodsThe research was arranged using nested (hierarchy design) with completely randomized design under a screen house at Karanganyar, Indonesia, from July to December 2019. The main factor was soil mulching (control; black plastic mulch, transparent plastic mulch, biodegradable mulch and rice straw mulch) with three replicates. The main factor was nested in the temporal hierarchy factor, namely harvest age which consisted of two levels, i.e., 80 and 100 days. The observation parameters were soil temperature of 10 min (maximum; mean; minimum and soil temperature-based Growing Degree Days, GDD) with sensors and logger; plant growth (plant height, number of leaves, fresh and dry weight); and secondary metabolites including phenol, flavonoids and antioxidant.ResultsThe results confirmed the order of the highest to the lowest mean soil temperature was resulted under the transparent plastic mulch > straw > black plastic mulch > control > biodegradable plastic mulch (26.69 > 26.29 > 26.10 > 26.07 > 25.68 °C, respectively). Overall, the harvest age 100 days resulted in higher plant growth, indicated by the higher fresh and dry weight of biomass, higher phenol and antioxidant contents than 80 days. Soil mulching, especially with plastic and biodegradable plastic mulches with long harvest age (100 days) effected into lower fresh and dry weight of plants. On the other hand, soil mulching indirectly resulted in lower phenol but higher flavonoid contents through higher soil temperature, while antioxidant contents were higher under the big soil temperature-based Growing Degree Day (GDD). The total phenol, flavonoids and antioxidant produced ranging from 193.75 to 412.50 mg GAE/ 100 g DW; 81.13 to 141.47 mg QE/ 100 g DW; and 1875.5–2144.4 µmol TE/g DW.ConclusionHigher maximum soil temperature resulted in lower phenol content, while higher minimum soil temperature and shorter harvest age increased total flavonoid. Longer harvest age produced more total phenol and antioxidant due to bigger soil temperature-based Growing Degree Day (GDD).Graphical