Dry Period Research Articles

Impact of heat stress during close-up dry period on performance, fertility and immunometabolic blood indices of dairy cows: prospective cohort study

This study aimed to investigate whether heat stress, as defined by the temperature-humidity index (THI) during the close-up dry period, had any impact on the productive performance, fertility, and immunometabolic blood indices of dairy cows in the subsequent lactation. Lactation performance was associated with increasing THI values on − 21, − 14, and − 7 d before calving resulting in decreased milk yield by about 2.30, 2.60, and 2.90 kg, respectively. The THI on the − 7 d before the calving was negatively associated with fertility parameters such as delayed first estrus postpartum, an elongated calving interval by approximately 32 d, a higher number of services per conception by 1.00, and an elongated artificial insemination service period, days open, and inter-calving period by about 20, 52, and 52 d, respectively. The study found that the immunometabolic blood indices were associated with increasing THI values during the close-up dry period. The study showed that exposing dairy cows to close-up dry period heat stress had negative consequences on performance, fertility, and immunometabolic blood indices in the subsequent lactation. Therefore, it is recommended that herd management and barn microclimate changes be implemented earlier, starting from the late dry period, to mitigate the negative impact of heat stress.

Assessment of water quality and identification of priority areas for intervention in Guanabara Bay basin, Rio de Janeiro, Brazil, using nonparametric and multivariate statistical methods.

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3-), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3-, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3- concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system.

Holocene hydroclimate synthesis of the Aegean: Diverging patterns, dry periods and implications for climate-society interactions

The Aegean region has a wealth of archeological data originating from a long and rich human history and is frequently used as a testbed for climate-society interactions at different time scales. Here, we summarize and synthesize 31 proxy records derived from lake and wetland sediments, marine sediments, and speleothems reflecting hydroclimatic conditions over the last 10,000 years in the Aegean. To enable comparison and numerical analyses, we convert each record into z-scores and average values into centurial and decadal bins. The long-term hydroclimatic trajectory is best described by individual trends in three sub-regions that periodically converge and diverge. The results from our analyses highlight the complex long- and short-term spatio-temporal patterns of the hydroclimate with periods of wetter and drier conditions. Statistical analyses of the last 3500 years, the most data-rich period, corroborate the complex climate picture. Three periods that appear dry in the regional averages, at 7250–6650 BP, 3350–2750 BP, and 1200–700 BP, highlight spatio-temporal variability and underpin the importance of local data when comparing hydroclimate data with archeological information. Considering the complexities and uncertainties in hydroclimate records, we advocate for careful consideration when utilizing these in studies of climate-society interactions, including transparency regarding their uncertainties and the relevance of each record to a respective archeological site. We have produced a freely available, simplified dataset that can be used by those interested in studying the region, as well as maps displaying climatic conditions during each century.

Cumulative effects of natural and anthropogenic processes on groundwater chemistry of a small karst island—case study of Vis (Croatia)

Many coastal and island communities depend on groundwater as the only source of freshwater, making it an invaluable resource. In the Mediterranean region, groundwater resources are highly vulnerable to natural and anthropogenic pressures, such as overexploitation, climate change, seasonal variations in precipitation, and seawater intrusion. Hence, an understanding of hydrogeological processes and groundwater chemistry is a basis for the sustainable management of coastal and island groundwater resources. Vis, a small and remote karst island in the Adriatic Sea, exhibits peculiar geological and hydrogeological settings, resulting in the island’s autonomous water supply. The current pumping capacity (maximum of 42 l/s) meets most of the demand, but intensive summer tourism and climate change exert high stress on groundwater resources during the dry season. Consequently, in the last decade, occasional reductions for consumers occurred. Monitoring of in situ physicochemical parameters and groundwater sampling for chemical and isotopic analyses were conducted from 2020 to 2023 at deep borewells, shallow dug wells, and springs. Hydrochemical interpretation indicated that groundwater chemistry was affected primarily by carbonate and sulfate rock dissolution, mixing with seawater, reverse ion exchange, and dedolomitization. The majority of groundwater samples exhibit Ca–HCO3 hydrochemical facies, followed by Na–Cl and mixed facies. The low percentage of seawater in the mixture indicated that seawater intrusion is not too extensive even during prolonged dry periods, implying a favorable hydrostatic regime with relatively small but sufficient groundwater reserves of the island’s aquifers, although the investigated period was characterized by significantly lower precipitation with respect to the 30-year average.

Evaluation of the PM2.5 concentrations in South America: Climatological patterns and trend analysis

This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022. The bilinear interpolation method was applied to the data resampling from 0.75° × 0.75°–0.25° × 0.25° spatial resolution, and subsequently, the climatological analysis was performed on seasonal and annual scales of PM2.5 concentrations for the 20-year series. The assessment comprised three main stages: 1) descriptive statistics, 2) seasonal and annual climatology patterns, and 3) trend analysis (Mann-Kendall - MK and Pettitt tests). Results indicated that the largest annual PM2.5 concentrations were found in the Amazon region and northern Paraguay due the El-Niño South Oscillation events (2003–2005, 2007, 2010, 2020, and 2022). The annual average PM2.5 concentrations exceeded the WHO recommended limit (5 μg m−3), ranging from 15.57 μg m−3 (1999) and 22.74 μg m−3 (2007 and 2010). The highest PM2.5 concentrations were observed in the Amazon and Chile (Santiago), reaching 160.4 μg m−3 annually and 177.8 μg m−3 seasonally (spring). Elevated seasonal PM2.5 concentrations were attributed to increased deforestation and wildfires associated with meteorological systems such as the Bolivian high and subtropical jets during dry periods (winter and spring). The MK test revealed a significant reduction in PM2.5 concentrations between 2003 and 2022, observed in Argentina (north), Brazil (arc of reforestation), Bolivia (south), and Chile (north). Reduction values varied between 0.5 and 10 μg m−3.year−1 annually and during winter, attributed to no-tillage practices in regions cultivating commodities. The Pettitt test also identified significant structural changes occurring in 2006–2008 and 2010–2011, aligning with areas highlighted in the MK test. This study provides valuable insights for air quality and environmental monitoring managers to mitigate PM2.5 pollution in SA.

Dependence of Drought Characteristics: Parametric and Non-parametric Copula Approach

Drought, which has harmful impacts both environmentally and economically, is one of the most devastating natural phenomena. In order to better understand and monitor the effects of drought, various methods have been developed in recent decades to quantify drought characteristics, with a primary focus on univariate drought indices. Mainly, drought characteristics are crucial to examine the impacts of drought in-depth on any specific area. This study endeavours to investigate univariate and bivariate drought indices using both parametric and non-parametric copula techniques. For that purpose, drought characteristics, such as duration, severity, mean intensity and peak intensity are analysed relying on different drought indices. The dependence among the main characteristics is evaluated and corresponding bivariate return period calculations are investigated. The data set used in this study is retrieved from monthly meteorological observations collected at five different Stations in Konya, located in the Central Anatolia Region of Turkey. Main numerical findings indicate the importance of using multiple drought indices for different geographical reasons for extreme dry periods.

Milk yield prediction in Friesian cows using linear and flexible discriminant analysis under assumptions violations

BackgroundThe application of novel technologies is now widely used to assist in making optimal decisions. This study aimed to evaluate the performance of linear discriminant analysis (LDA) and flexible discriminant analysis (FDA) in classifying and predicting Friesian cattle’s milk production into low (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:<$$\\end{document}4500 kg), medium (4500–7500 kg), and high (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:>$$\\end{document}7500 kg) categories. A total of 3793 lactation records from cows calved between 2009 and 2020 were collected to examine some predictors such as age at first calving (AFC), lactation order (LO), days open (DO), days in milk (DIM), dry period (DP), calving season (CFS), 305-day milk yield (305-MY), calving interval (CI), and total breeding per conception (TBRD).ResultsThe comparison between LDA and FDA models was based on the significance of coefficients, total accuracy, sensitivity, precision, and F1-score. The LDA results revealed that DIM and 305-MY were the significant (P < 0.001) contributors for data classification, while the FDA was a lactation order. Classification accuracy results showed that the FDA model performed better than the LDA model in expressing accuracies of correctly classified cases as well as overall classification accuracy of milk yield. The FDA model outperformed LDA in both accuracy and F1-score. It achieved an accuracy of 82% compared to LDA’s 71%. Similarly, the F1-score improved from a range of 0.667 to 0.79 for LDA to a higher range of 0.81 to 0.83 for FDA.ConclusionThe findings of this study demonstrated that FDA was more resistant than LDA in case of assumption violations. Furthermore, the current study showed the feasibility and efficacy of LDA and FDA in interpreting and predicting livestock datasets.

Changes in tropical leafing behaviour with climate change over nine decades: A case study from the Singapore Botanic Gardens

Societal Impact StatementLong‐term phenology data is crucial to elucidate the effects of climate change on plants, but such efforts are lacking in the tropics. Historical records at the Singapore Botanic Gardens and ongoing phenological monitoring have allowed us to study leafing behaviour and its association with changing rainfall patterns over nine decades in equatorial Singapore. We found that the leafing behaviour of trees in our tropical site changed between 1927 and 2022. This study was made possible by assiduous past and present record‐keeping, and demonstrates the potential of botanical gardens as sentinels of environmental change, which is especially important in the tropics.Summary Understanding tropical phenology and the potential impacts of a changing climate requires long‐term monitoring but such efforts are rare in the tropics. We took advantage of the availability of historical phenological and weather records at the Singapore Botanic Gardens and ongoing phenological monitoring to ask whether leafing behaviour has changed over an exceptionally long period of about nine decades, and whether these changes are associated with changing rainfall patterns in supposedly aseasonal Singapore. Records of leaf flush intervals from 1927 to 1939 in the Singapore Botanic Gardens were compared against leaf flush intervals calculated from present‐day phenological monitoring conducted since 2016. Daily rainfall was modelled as a predictor of present‐day leaf flushing events using moving‐window analyses. Leaf flush intervals have shortened considerably for most of the species that were monitored in the two periods. Mean daily rainfall over a window of 1 to 4 months before was negatively associated with probability of leaf flushing for five species and positively associated with four out of a total of 23 species analysed. Long‐term rainfall records from 1929 to 2022 show that February has become drier and May has become wetter since the 1960s. The results consistently support that more distinctive dry and wet periods today compared to nine decades ago have resulted in more frequent leaf flushing, which may have implications for plant‐herbivore interactions, nutrient cycling and consequently plant health and ecosystem resilience. We also demonstrate the potential and importance of supporting long‐term research in botanical gardens in the tropics.

Patterns of non‐migratory fish metacommunity from Neotropical floodplains: Local mechanisms, regional scale and hydrological dynamics

AbstractThe spatial distribution of biological communities may be regulated by environmental and spatial processes, and the intensity of these processes depend on the species characteristics, spatial heterogeneity and spatial extent. We investigated the relationship between the non‐migratory fish metacommunity with environmental and spatial variables using the variance partitioning procedure. We used data from lakes of four Neotropical floodplains: Amazon, Araguaia, Pantanal and Paraná. We hypothesised that the processes that drive the spatial distribution of non‐migratory fish in floodplain systems depend on connectivity driven by flood pulse. We predict that spatial variables are less important to explaining the fish community structures during rainy periods when there is no dispersal limitation. In contrast, environmental variables influence these structures during rainy and dry periods. During dry periods, metacommunities depend on species' dispersal abilities, but during rainy and dry periods, spatial distribution depends on the environmental filters selected by each species. For most of the environmental variables, it was possible to observe more similarity in the rainy period. Our results showed different patterns of fish communities for the lakes of the four floodplains systems, which led us to reject our hypothesis. Non‐migratory fish remain in their natal environment and perform only small lateral movements. In this scenario, we found a little influence from spatial variables in both periods and a great contribution of environmental variables for Amazon (16%) and Pantanal (32%) floodplains in structuring fishes' distribution in the rainy period.

SOME FEATURES OF CONSTRUCTING DISPLACEMENT CHARACTERISTICS AND WATERING DYNAMICS

The term «displacement characteristic» (DCh) was first proposed by D. A. Efros in 1959 to indicate the dependence of the accumulated oil withdrawal on the accumulated liquid withdrawal. Subsequently, DChfound wide application in the analysis of oil field development. Many authors proposed various modifications of the DCh coordinates, but directactual measurements of development parameters were always used - annual, accumulated measurements of oil, liquid, water production, water cut, liquid, oil, and water flow rates. Characteristics of oil displacement are called graphical dependences of accumulated oil production on accumulated or current values of liquid or water production, constructed based on actual data. With the help of cold water, three tasks were solved: assessment of recoverable oil reserves, calculation of the technological effect of geological and technical measures, and calculation of technological development indicators. But the main condition for application was the immutability of the development system.Recently, dependences that use parameters such as selection from geological or recoverable oil reserves have come to be classified as chemical dependencies. These dependencies have nothing to do with DCh. Justification of geological and recoverable oil reserves is an independent task; moreover, with the help of chemical recovery, recoverable reserves are found under the existing development system. Against the background of such a confusion of concepts, the definition of the concept of «standard» chemicals is introduced, which has no real meaning for an oil development facility.The dynamics of water cut refers to the change in water cut over time, for example, depending on the time of development of reserves, geological or recoverable. The dynamics of watering in this case has nothing to do with the cold water.The dry period is of great importance when developing an oil field. With the same finaloil recovery, the longer the water-free period, the better the economic indicators.Using the Buckley-Leverett function todetermine water cut is only possible when the flow is two-phase, oil and water. It is impossible to determine the anhydrous period using the Buckley-Leverett function, since it does not exist, therefore, constructing the dynamics of water supply only based on this function is incorrect.When conducting laboratory experiments on core material, in which the heterogeneity compared to the formation is minimal and can be identified with a one-dimensional flow, the anhydrous period is significant and reaches from 0.74 to 0.94 units. of the total volume of displaced oil.For a single-layer field, where there is only one homogeneous layer, the heterogeneity is greater than for the core, but less than for a heterogeneous development object. We can talk about an analogue of a two-dimensional flow with some degree of convention.Considering that these are not laboratory studies, where measurements are carried out more accurately, but production ones, the low-water-cut period (up to 10 %) is 0.4 of the total oil production from wells with simultaneous commissioning of wells. If the wells were commissioned at different periods of time, as is always the case in fields, then the dynamics of water supply are completely different, and water supply begins earlier than when all wells are commissioned simultaneously.

Assessment of meteorological drought impacts on rainfed agriculture using remote sensing-derived biomass productivity.

Assessing drought impacts is necessary for pursuing sustainable development goals relevant to food security and land degradation. Data availability is a major restriction and remote sensing has been promoted for this purpose. Version 3 of WaPOR has been released in 2023, which provides global coverage of remote sensing-derived water productivity indicators and could allow improved analysis of drought impacts, but validation is still needed. This study explores the utility of remote sensing-derived productivity data from WaPOR as a proxy indicator for agricultural drought impacts. The analysis utilized (1) production surveys, (2) meteorological measurements for drought analysis, and (3) remote sensing-derived gross and net biomass water productivities (GBWP & NBWP) and total biomass production (TBP). All layers were analyzed against the Standardized Precipitation and Standardized Precipitation Evapotranspiration Indices (SPI and SPEI) over drought-vulnerable locations in Irbid and Madaba governorates in Jordan. Strong and significant correlations (R2 0.5-0.8, P < 0.05) were obtained between drought intensities and GBWP and NBWP layers, particularly in the May-Sep periods. These correlations were higher than previously tested remotely sensed indicators for agricultural drought impacts. Water productivity and biomass production averages were lower during drier periods and higher during wet periods, but pairwise testing did not reveal significant differences. There is sufficient evidence that WaPOR data demonstrates behavior that reflects agricultural response to drought, and further assessment in other agroclimatic zones is recommended. This could potentially allow for enhanced evaluation of management strategies, decision support, and policy recommendations for drought mitigation.

Improving streamflow forecasting in semi-arid basins by combining data segmentation and attention-based deep learning

The increasing threats of flash floods and water scarcity in semi-arid regions necessitate high-quality streamflow forecasting with process-based or data-driven models. However, temporal heterogeneity of hydrological patterns and infrequent flood events present challenges for accurate streamflow forecasting. To address this issue, we purpose an effective modeling approach that combines a novel data segmentation method, BPX, with an attention-based deep learning (DL) model. BPX integrates Bai-Perron analysis and XGBoost to accurately identify dry and wet periods from hydro-meteorological variables both in the past and future times. The DL model introduces an attention mechanism to temporal convolutional network (TCN) to enhance its ability in handling heterogeneous time-series data. We demonstrate the effectiveness of our approach using nearly 20 years of hydro-meteorological data from the Wei River basin in China. Various modeling methods are compared, including two process-based models (Xinanjiang and GR4J) and four DL models (classical RNN, GRU, LSTM and TCN) with or without the attention module. After systematic benchmarking, it is found that both the BPX segmentation and the attention mechanism can improve streamflow forecasting with DL. Among the various modeling approaches, BPX-TCN exhibits the best overall performance throughout the prediction period, achieving the highest Kling-Gupta efficiency of 0.91 (compared to 0.45–0.88 for the other models), while BPX-TCN-attention provides more accurate predictions of floods and multi-step predictions. In similar applications in semi-arid regions, our approach may serve as a valuable reference where temporal heterogeneity is significant and poses challenges to traditional modeling methods.

Groundwater sustainability in India through nonrice-dominated cropping pattern.

Over-exploitation of groundwater for irrigation caused rapid groundwater depletion in north India, leading to food and water security challenges. However, the crucial role of changing cropping patterns on groundwater savings under the observed and projected warming climate remains unexplored. Here, we show that altering the existing rice-dominated cropping systems in India can be a potential solution for groundwater sustainability under the current and future climate. Satellite and model-based estimates show that north India lost ∼336 and 297 km3 of groundwater, respectively during 2002-2022. We developed optimized crop switching scenarios for groundwater savings considering nutritional requirements, farmers' profit, and crop production. Crop switching considering all the three targets (crop switch one: CSI) and allowing rice replacement with alternate crops (crop switch two: CSII) could save 45 and 91 km3 groundwater, respectively in north India during the observed climate (2002-2022) compared with the current cropping pattern. Altering the current cropping pattern can lead to substantial groundwater savings under the projected future climate without comprising nutritional targets and farmers' profit at the state level. Replacing 37% area of rice with other crops (CSII) can recover 61 to 108 km3 groundwater compared with -13 to 43 km3 with current cropping pattern under the 1.5-3 °C global warming levels. Similarly, under the CSI scenario, 36 to 86 km3 groundwater can be recovered in the future warming world. Moreover, the benefits of crop switching in groundwater saving are higher during the prolonged dry periods compared with the baseline under the warming climate. Therefore, crop switching offers substantial benefits for groundwater sustainability under the current and projected future climate in India.

Effect of prolactin concentration during the dry period on the subsequent milk production of dairy cows

Shortening the dry period has a negative impact on milk production of the following lactation. One possible explanation is that a period of low prolactin (PRL) concentration is necessary to restore mammary gland milk production capacity. Therefore, the objective of this study was to determine the effect of lowering blood PRL level on subsequent lactation milk production. In this experiment, quinagolide was used to inhibit PRL secretion during the dry period. Thirty Holstein cows were randomly assigned one of 3 dry period managements: a conventional (60 d) dry period (CD) and 2 short (35 d) dry period treatments (SD). Short dry period cows received either water (SDwater) or quinagolide (2 mg, SDquin) injections twice daily from dry-off until 14 d before calving. Cows were followed during the first 20 wks of the subsequent lactation. When CD cows were dry but SD cows were lactating, concentration of PRL was lower in the CD cows than in the SD cows. During the injection period, PRL of SDquin cows was lower than that of the other treatments and was greater in the blood of SDwater than in that of CD cows. After the injection period until calving, no difference in PRL concentration was observed between treatments. After calving, PRL concentration of the SDquin cows was greater than those of CD and SDwater cows. During the first 20 wks of lactation, energy corrected milk (ECM) was lower in SDwater cows than in CD and SDquin cows. The ECM of the latter groups were not different. During the same period, mammary expression of genes related to milk synthesis, pro-apoptotic genes, as well as the expression of the short and long isoforms of the PRLR genes were not affected; however, the expression of SOCS3 gene tended to be lower for the SDquin than the SDwater cows. Lowering the PRL level during short dry period restored milk production to the level normally observed after a conventional dry period, which suggests that higher PRL levels during short dry period are the cause of the lower milk production after a short dry period. Ultimately, strategies to lower blood PRL level may help the adoption of short dry period.

Genetic and Phenotypic Trends for Production and Reproduction Traits in Murrah Buffaloes.

The objective of the study was to estimate genetic and phenotypic trends for first lactation production and reproduction traits in Murrah buffaloes. The information of pedigree and targeted traits of 640 Murrah buffaloes was collected for the period from 1997 to 2020. The first lactation production traits included first lactation milk yield (FLMY), 305 days first lactation milk yield (305FLMY), first lactation length (FLL), first lactation peak yield (FPY) whereas reproduction traits included first service period (FSP), first calving interval (FCI) and first dry period (FDP). Genetic and phenotypic trends were estimated. Phenotypic trends for FLMY, 305FLMY, FLL and FPY exhibited as 36.96 ± 8.58 kg/year, 31.93 ± 8.34 kg/year, 1.47 ± 0.55 days/year and 0.12 ± 0.02 kg/year, respectively and respective genetic trends exhibited as 3.73 ± 1.67 kg/year, 1.94 ± 0.76 kg/year, -0.15 ± 0.07 days/year and 0.01 ± 0.01 kg/year, respectively. It was revealed that there were significant (p < 0.05) and positive phenotypic trends for all production traits while genetic trends were significant (p < 0.05) for FLMY and 305FLMY traits. The phenotypic trends of studied reproductive traits indicated that only FDP trait significantly (p < 0.01) decreased (1.87 days/year) over time. For FSP and FCI traits, nonsignificant (p > 0.05) genetic and phenotypic trends indicated no change over time. This study highlighted potential challenges in maintaining reproductive efficiency alongside productivity improvements in Murrah buffaloes.

Longitudinal characterization of the muscle metabolome in dairy cows during the transition from lactation cessation to lactation resumption

Skeletal muscle is vital in maintaining metabolic homeostasis and adapting to the physiological needs of pregnancy and lactation. Despite advancements in understanding metabolic changes in dairy cows around calving and early lactation, there are still gaps in our knowledge, especially concerning muscle metabolism and the changes associated with drying off. This study aimed to characterize the skeletal muscle metabolome in the context of the dietary and metabolic changes occurring during the transition from the cessation of lactation to the resumption of lactation in dairy cows. Twelve Holstein dairy cows housed in tie stalls were dried off 6 weeks (wk) before the expected calving date. Cows were individually fed ad libitum total mixed rations composed of grass silage, corn silage, and concentrate during lactation and of corn silage, barley straw, and concentrate during the dry period. The metabolome was characterized in skeletal muscle samples (M. longissimus dorsi) collected on wk -7 (9 d before dry-off), -5 (6 d after dry-off), and wk -1, and 1 relative to calving. The targeted metabolomics approach was conducted using the MxP Quant 500 kit (Biocrates Life Sciences AG) with liquid chromatography, flow injection, and electrospray ionization triple quadrupole mass spectrometry. Statistical analysis on the muscle metabolite data was performed using MetaboAnalyst 5.0, which allowed us to conduct various multivariate analyses such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), informative heat map generation, and hierarchical clustering. The statistical analysis revealed a clear separation between pregnancy (wk -7, -5, and -1) and post-calving (wk 1). Starting 5 wk before calving and continuing through the first wk thereafter, the concentration of 3-methylhistidine (3-MH) in the muscle increased. This coincided with an increase in the concentrations of 11 AA (Phe, His, Tyr, Trp, Arg, Asn, Leu, Ile, Gly, Ser, and Thr) in the first wk after calving, whereas Gln decreased. l-arginine pathway metabolites (homoarginine, ornithine, citrulline, and asymmetric dimethylarginine), betaine, and sarcosine followed a similar pattern, increasing from wk -7 to -5, but decreasing from wk -1 to 1. The transition from pregnancy to lactation was associated with an increase in concentrations of the long-chain acylcarnitine species C16, C16:1, C18, and C18:1 in the muscle, whereas the concentrations of phosphatidylcholine and sphingomyelin in the muscle remained stable. The significant changes observed in the metabolome mainly concerned the AA and AA-related metabolites, indicating muscle protein breakdown in the first wk after calving. The metabolites produced by the L-Arg pathway might contribute to regulating skeletal muscle mass and function in periparturient dairy cows. The elevated concentrations of long-chain acylcarnitine species in the muscle in the first wk after calving suggest incomplete fatty acid oxidation, likely due to insufficient metabolic adaptation in response to the fatty acid load around the time of calving.

Precipitation measurements experiment using microwave links in Eastern China from October 2020 to March 2022

Precipitation is an important piece of information needed in many areas such as transportation, military and agriculture, and microwave links have proven to be an effective means of acquiring it and can be used as a complementary means for professional precipitation measurement instruments such as rain gauges, weather radar, rain measuring satellites, etc. In this paper, two microwave links (at 26 GHz, both vertically polarised) located across a river in eastern China and a nearby OTT PARSIVEL disdrometer were used to carry out a precipitation observation experiment from October 2020 to March 2022 (March to November 2021 for liquid precipitation, others for possible non-liquid precipitation). The applicability of the dry and rainy period identification methods (standard deviation method and correlation coefficient method) based on this link data is analyzed first. In addition, a wet antenna attenuation correction model based on a Long Short-Term Memory (LSTM) neural network was applied to non-winter precipitation inversion. The results show that the rain rate and cumulative rainfall calculated from the two links are in good agreement with that calculated by the disdrometer data. Furthermore, quantitative inversion of winter precipitation using two microwave links is performed, and the variation characteristics of the link levels during non-liquid precipitation periods are also analyzed.

Precipitation in Kathmandu Valley: A Spatial-temporal Study

Over the past decade, advancements in atmospheric research have significantly improved the analysis of precipitation monitoring, understanding climate trends and variability, and identifying climatic anomalies associated with dry and wet periods. A key objective of this research is to enhance the understanding of precipitation patterns in the Kathmandu Valley, particularly in relation to the placement of gauges for long-range predictions and subsequent forecasting challenges. This study focuses on the evolving patterns of precipitation across the three districts within the Kathmandu Valley, the most developed and densely populated area in Nepal, encircled by four mountain ranges. We employ interpolation methods in ArcGIS Pro to identify, estimate, and forecast precipitation trends, and use the Mann-Kendall test to reveal statistically significant trends and provide insights into broader climatic processes. The research analyzes trends in both seasonal and annual precipitation and temperature across four time periods—pre-monsoon, monsoon, post-monsoon, and winter—using the Mann-Kendall test. The analysis reveals a decreasing trend in monthly rainfall from 2003 to 2017 at the majority of stations (14 stations out of 19), with areas along the valley slopes receiving significantly more precipitation compared to the central valley. The results of this study contribute to a deeper understanding of climate change in the Kathmandu Valley.

Irrigation and oil palm empty fruit bunch mulch enhance eggplant growth, radiation interception and dry matter yield

Organic mulching is a well-known management practice that conserves soil water and nutrients as well as increases crop yield. Nonetheless, research on combined organic mulching using oil palm empty fruit bunch (EFB) and irrigation is limited. Field-based experiments were conducted over three seasons to test the sole and combined effects of EFB as organic mulch and irrigation on the growth, total dry matter yield (TDMY), accumulated intercepted photosynthetically active radiation (AIPAR), and radiation use efficiency (RUE) of African eggplant (Solanum aethiopicum L.) in a low fertile tropical sandy clay loam soil. Air-dried EFB was used as an organic mulch by spreading it on the soil surface at rates of 0 (EFB0), 20 (EFB20), and 40 t ha−1 (EFB40), and either fully-irrigated (I100), deficit-irrigated (I40), or non-irrigated (I0). The I100 plots were irrigated to field capacity (FC) every 3–4 days based on PR2 Profile Probe measurements and the resultant irrigation volume supplied to the plants via drip irrigation tubes. The I40 plots received 40 % of the water given to the I100 plots, and the I0 plots were solely rain-fed. At the end of the third season, the 40 t ha−1 EFB-mulch increased soil pH, electrical conductivity (EC), soil organic carbon, potassium, cation exchange capacity, and the soil’s specific surface area. In the first season, all the measured eggplant growth and yield parameters were neither responsive to irrigation only, EFB-mulch only, or both. In the second and third seasons, the EFB20 and EFB40 treatments significantly (p < 0.05) increased leaf chlorophyll content index (LCCI), photosystem II (Fv/Fm ratio), absolute performance index (PIabs), TDMY, AIPAR, and RUE compared to the non-mulched control treatment. Soil pH was high in the EFB-mulched plots and correlated positively with TDMY and AIPAR. The I100 significantly improved LCCI, Fv/Fm, PIabs, and TDMY during the second season. In the third season, a highly significant interaction between irrigation and mulching was detected on TDMY, AIPAR, RUE, LCCI, Fv/Fm ratio, and pH (H2O). This indicated a positive effect on soil nutrient availability especially phosphorus as TDMY and AIPAR correlated with soil pH. The I100 and I40 significantly increased AIPAR by 48.1 % and 37.2 %, and RUE by 26.7 % and 11.0 %, respectively, compared to I0 during the third season. The total dry matter yield of the African eggplant was enhanced by EFB-mulch, with the effect increasing over up to three growing seasons, especially when combined with irrigation during dry periods.

Update on ketosis in dairy cattle with major emphasis on subclinical ketosis and abdominal adiposity.

The metabolic changes that occur during the early post-partum period in dairy cows can indeed lead to an imbalance in energy utilization, resulting in the production of excessive ketone bodies. This can have detrimental effects on the cow's health and milk production, leading to economic losses for dairy producers. The release of non-esterified fatty acids into the blood due to increased lipolysis is a key factor in the development of ketosis. Abdominal adiposity is a key factor on these outcomes in modern dairy cows. The redirection of energy and glucose towards lactose synthesis and milk yield leaves a deficit of gluconeogenic precursors, leading to the conversion of acetyl-CoA into ketone bodies instead of entering the Krebs cycle. These ketone bodies, including acetone, acetoacetate and β-hydroxybutyrate, accumulate in the blood and can be detected in various bodily fluids, such as urine, blood and milk, allowing for diagnostic testing. Prevention is indeed crucial in managing ketosis in dairy cattle. Supplementation of propylene glycol in the diet or the use of monensin, either in the diet or in the form of a slow-release bolus, can help prevent the occurrence of ketosis. However, avoiding high body condition (subcutaneous fat) and excessive abdominal adiposity during the dry period and parturition plus an adequate cow comfort are fundamental tasks to avoid ketosis and related disorders. These interventions aim to provide additional energy sources or enhance the cow's ability to utilize energy efficiently, thus reducing the reliance on excessive lipolysis and ketone body production.