Rainfall Conditions Research Articles

An Evaluation of the Sediment Reduction Benefits of Soil and Water Conservation Measures under Extreme Rainfall Conditions in the Loess Plateau in China Based on a Distributed Soil Erosion Model

Since the 1970s, comprehensive control measures on soil erosion in the headwater region of the Loess Plateau have been carried out. Quantitative evaluation of the benefits of soil and water conservation measures during extreme rainstorms is of great significance for the comprehensive management of the catchments. In this study, a systematic modeling methodology for evaluating the effects of soil and water conservation measures on sediment reduction was developed based on a distributed soil erosion model (DSEM). Taking the Chabagou basin in the Loess Plateau as the study area, the tested DSEM was used to simulate soil erosion and sediment yield during an extreme rainstorm under two scenarios, the uncontrolled condition and the controlled condition with soil and water conservation measures implemented. The results showed that DSEM could successfully simulate soil and water losses and evaluate the effects of soil and water conservation measures during extreme storm events. The evaluated results showed that each soil and water conservation measure had the specific function of sediment reduction. And under rainstorm conditions, the effect of engineering measures on sediment reduction was greater than that of forest and grass measures.

Modeling the influences of rainfall conditions on nitrogen removal effects of a permeable pavement system

Permeable pavement systems are important facilities for alleviating runoff pollution. However, few studies have focused on outflow nitrogen processes under varying rainfall conditions. In this study, we proposed a permeable pavement system model that can simulate the nitrogen transformation processes. The model was verified with data on outflow rates and outflow nitrogen concentrations (ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), and total nitrogen (TN)) processes for six consecutive rainfall events of a permeable pavement system in Shenzhen, China. Based on the validated model, scenarios were designed to simulate the influences of rainfall conditions on nitrogen removal effects at the single rainfall event and annual scale, respectively. The results indicate that: (i) the model can explicitly describe hydrological and nitrogen processes of the system, and the Nash Sutcliffe Efficiency and percent bias of outflow rates and outflow nitrogen concentrations range from 0.5 to 0.9 and from −22.4% to 24.9%, respectively; (ii) the sensitivity analysis reveals that the empirical coefficient (C) related to evaporation in the gravel layer is sensitive at the annual scale but not at rainfall events scale. Parameters related to mineralization and microbial assimilation (i.e., mineralization rate constant in the gravel layer (k3N,mine), microbial assimilation rate constant of NH4-N, NO3-N, and ON (k1N,bio, k2N,bio, k3N,bio)) are sensitive annually but not at rainfall events scale; (iii) in the single rainfall event simulations, nitrogen removal efficiencies decrease with increasing rainfall return periods but is not significantly affected by rainfall peak coefficients. Nitrogen removal efficiencies improve notably with antecedent dry period extension for single rainfall event with antecedent dry period less than 2 days. In addition, we found that the outflow process significantly affects the outflow nitrogen concentrations process. In annual-scale simulations, extending the antecedent dry period only significantly boosts runoff reduction efficiency and nitrogen removal efficiencies for rainfall events with less than 10 mm.

Exploitation of heterosis in chilli using genetic male sterile lines for red fruit yield with special reference to high rainfall conditions

High-quality red/dry chilli for spice, pharmaceutical and medicinal purposes is a major goal in chilli breeding. The male sterile lines have greater potential for the exploitation of heterosis in chilli to achieve this objective. Genetic male sterile lines with special traits like destalking and ability to withstand high rainfall were involved in heterosis breeding to identify hybrids for commercial and industrial purposes. Forty F1 hybrids were developed by crossing 4 diverse GMS lines with 10 testers using Line × Tester mating design to estimate heterosis, combing ability and gene action. The experiment involving 14 parents and 40 F1s, along with standard variety ‘CH-27’ was laid out in α-lattice square design in three replications during summer 2020 and 2021. The GMS lines MS 9-2 and MS 26-1 along with testers DPCh 10, VVG, DPCh 40 and Him Palam Mirch-2 showed significant GCA for marketable red/dry fruit yield and majority of their component traits. Ten F1 hybrids were identified with superiority for fruit yield based on mean performance, significant heterosis and SCA effects, providing an opportunity to utilize them in value-added products and dried spice purposes viz., MS 9-2 × HPM-2, MS 11-2 × DPCh 40, MS 9-2 × DPCh 40 and MS 9-2 × DPCh 101 with erect fruiting habit and that of MS 9-2 × DPCh 10, MS 26-1 × DPCh 10, MS 9-2 × PBC 535, MS 26-1 × VVG, MS 29-2 × DPCh 10 and MS 26-1 × DPCh 22- C with pendent fruits. The non-additive gene action was predominant in the expression of fruit yield, total red fruits/plant, oleoresin and capsanthin. A significant positive correlation among SCA, economic heterosis and per se performance is an indicative to identify superior hybrids. Multi-location testing of these hybrids shall pave way to exploit them commercially by making them available to the farmers.

Multisource remote sensing monitoring and analyzing for land subsidence and crop growth in a coal mining area under different rainfall conditions

Coal has been crucial in driving economic development and production construction. However, the mining-induced subsidence may cause irreversible damage to the surrounding environment of vegetation growth. Meanwhile, with the worsening of global warming, the frequency and intensity of extreme water-related weather events, such as droughts and excessive rainfall, are on the rise, which leads to heightened impacts on ecosystems and agricultural production. Consequently, extreme water-related weather, the distribution of land subsidence, and its effect on vegetation have attracted significant attention. Based on the Sentinel-1 radar data and Sentinel-2 multispectral data from 2017 to 2022, the SBAS-InSAR technology, the object-oriented classification, and the Normalized Difference Vegetation Index (NDVI) were employed respectively in the study to obtain the spatial-temporal evolution of land subsidence, subsidence-induced water, and crop growth in Tiefa mining area, a representative coal mining area in Northeast China. Moreover, the relationship between land subsidence, subsidence-induced water, and vegetation change was analyzed combined with summer precipitation data. The results showed that: (1) The average cumulative subsidence of the mining area was 256.8 mm, and the subsidence area was 42.525 km2 for the six years. Among them, the heaviest subsidence reached a maximum of 380.5 mm in 2022, and the largest subsidence area was 20.109 km2 in 2017. (2) When the rainfall was excessive, the area of subsidence-induced water would increase sharply, with a proportion jumping to 9.71% from 5.37%, which indicated the subsidence would further amplify the destructive effect of excessive rainfall and waterlogging on land resources. (3) In addition to the existing water pits, ground cracks and shallow subsidence pits appeared under the influence of underground coal mining. The direct impact of ground cracks on crops was not apparent, while the effect of subsidence pits on crops under different rainfall conditions was dual character. In dry years, crops in the subsidence pits could grow better due to higher soil moisture. In wet years, crops in the subsidence pits would suffer the more severe waterlogging. The research results are of great significance for further understanding the influence of coal mining on surface vegetation in mining areas in Northeast China.

A rainfall prediction model based on ERA5 and Elman neural network

The heightened frequency and intensity of heavy rainfall, brought about by global climate warming, significantly affect various regions. Rainfall prediction plays a pivotal role in ensuring societal security and fostering development. There has been limited exploration of the impact of input parameters on the accuracy of rainfall predictions. Despite the advantages of Elman neural network models in handling non-linear relationships and spatiotemporal data, their application in weather forecasting is restricted. This paper assesses the accuracy of the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) with a dataset from meteorological stations. It introduces a novel rainfall forecasting model based on the Elman neural network and ERA5 reanalysis data. The study also identifies optimal input parameters through factor correlation analysis. Experimental results showcase the precision and stability of ERA5 across various rainfall conditions. Meteorological parameters, such as Precipitable Water Vapor (PWV), exhibit noticeable correlations with temporal variables and precipitation volume. The seven-factor model, including PWV, Zenith Tropospheric Delay, temperature, relative humidity, day-of-year, and hour of day, outperforms in precision evaluation. It achieves a mean critical success index of 57.06 %, a correct forecast rate of 91.39 %, and a false alarm rate of 39.48 %. This rainfall forecasting model introduces a novel approach and empirical research to enhance predictive accuracy, holding significant implications for the amelioration of meteorological alert systems, risk mitigation, and the safeguarding of life and property.

On-site monitoring of expansive soil slope protected by soilbags based on the universal Beidou monitoring system

To explore an effective method for deformation monitoring and behavior prediction of expansive soil slope, field tests are conducted for a flexible slope protection scheme with soilbags that has been implemented in an expansive highway soil slope. A new monitoring system, i.e., the universal Beidou deformation monitoring system, is developed to overcome the limitations of traditional Global Navigation Satellite System (GNSS) software and hardware, simplify the hardware structure and realize the power sharing mode; furthermore, this system can create and upload a large amount of monitored data to a cloud platform to enable real-time calculation. Compared with traditional GNSS, the volume of equipment required is reduced by approximately 75%, and the cost is reduced by approximately 80%. Secondly, a multilevel safety early-warning evaluation system is constructed by integrating the monitoring results of the universal Beidou deformation monitoring system, bag damage states, rainfall conditions, and slope fissure development; additionally, a deformation early-warning mechanism of flexible support of soilbags was established. Finally, the deformation and collapse of flexible supports of soilbags can be successfully predicted in the field. This research on flexible support of soilbags provides new ideas and methods of deformation monitoring, safety evaluation, and early warning for expansive soil slopes.

Construction of landslide warning by combining rainfall threshold and landslide susceptibility in the gully region of the Loess Plateau: A case of Lanzhou City, China

Regional forecasting of the landslide rainfall threshold is complex, and the empirical prediction of the rainfall threshold relies on historical statistical data. To obtain the intensity I ∼ duration (D) rainfall threshold, Lanzhou City was selected as the research object, and a field investigation, 3S technology, mathematical statistics and a Python language environment were employed to develop a set of automatic rainfall threshold calculation programs. Factor elimination, the information value method, weight of evidence, the frequency ratio, the density method and receiver operating characteristic (ROC) curve were applied to obtain landslide susceptibility, and the I ∼ D rainfall threshold (temporal probability) and landslide susceptibility (spatial probability) were combined to construct regional landslide rainfall threshold warning levels (red, orange, yellow, and blue). The results showed the rainfall duration of historical landslides ranged from 0.99 to 216 h, thereinto, it was accounted for 92.78 % with that of between 6 and 96 h, and the rainfall intensity was between 0.11 and 23.6 mm/h, and the α and β of the rainfall threshold curve was gradually increased with an increase in the rainfall threshold probability. Additionally, when the rainfall condition probability was the same, landslides occurrence was more frequent in Chenguan District, where there are large areas of bare land, and slope and annual rainfall are less than 10 °and 300 mm, respectively. Fifteen, nine, and six factors were used to obtain landslide susceptibility, and the accuracy of the ROC ranged from 0.78 to 0.80 under different methods and different factor combinations. The spatial distribution of landslide susceptibility was very similar. There were 35, 12, 25, and 108 landslides associated with red, orange, yellow, and blue levels, respectively, under the spatial–temporal probability, but 30, 26, 54, and 70, respectively, under temporal probability. Under the different conditions, there were 89, 69, and 22 landslide points with unchanged, increased, and decreased warning levels, respectively. The ability and accuracy of rainfall threshold warning and prediction were effectively improved by integrating the temporal and spatial probabilities of the rainfall threshold.

Impact of Extreme Rainfall on High-Speed Rail (HSR) Delays in Major Lines of China

Abstract Punctuality monitoring and analysis aim to optimize resource allocation for enhancing rail traffic performance and quality. Adverse weather conditions, particularly heavy precipitation events, are recognized as significant drivers of delays and reduced punctuality of the rail system. This study addresses two key research questions using high-speed rail (HSR) as an example—what is the impact of rainfall on HSR’s delay and to what extent are HSR vulnerable to rainstorms. The data for the study were collected from the HSR on the major lines of eastern China in the rainy season of 2015–17 which lasted from May to October. High-resolution precipitation data are integrated with nonspatial HSR operational data using GIS to create composite grids covering buffer zones around HSR lines. These grids match the spatial scale of historical hourly precipitation data and enable regression analyses to assess how precipitation affects HSR operations. The results indicate that extreme rainfall significantly contributes to delays and reduced punctuality, with varying impacts observed across different HSR lines. Specifically, daily areal precipitation significantly delays services on the Hangzhou–Shenzhen and Nanjing–Hangzhou HSR lines. Rainfall intensity has a more pronounced impact on delay services of the Beijing–Shanghai HSR, while extreme precipitation most frequently affects the Shanghai–Nanjing and Jinhua–Wenzhou HSR lines. The case analysis enhances understanding of HSR vulnerability to heavy rainfall conditions and recommends regional adaptation strategies to manage climate-related uncertainties.

The potential to reduce runoff generation through improving cropping and tillage practices in a sub-humid continental climate

Agricultural sustainability is threatened by both water deficit and water excess, especially at the presence of extreme meteorological events resulting from climate change. However, there has been lack of demonstrations on management options with long-term values for agricultural adaptation to runoff. Using 20 years of monitoring data (1993‒2012) for two experimental fields in the Canadian Prairies as a case study, we quantified the effects of rainfall characteristics, crop type and biomass, and tillage on growing-season runoff generation using regression analyses and thereafter scenario comparisons. With growing-season gross rainfall ranging between 183 and 456 mm, runoff responses varied between 0 and 59 mm. Over the 20-year study period, 70%‒74% of the growing-season runoff was generated by rainfall events > 100 mm. Compared to high-intensity tillage, long-term conservation tillage reduced both overall runoff and runoff in large events likely by improving water infiltration. Under both tillage methods, growing-season runoff significantly increased with increasing rainfall but decreased with increasing biomass (R2 range: 0.40‒0.58; p range: 0.0007‒0.02). At the event level, the rainfall-runoff relationship followed a piecewise regression model (Cd = 0.82; p < 0.0001; “breakpoint” rainfall event = 105 mm), in which runoff increased slowly before reaching the “breakpoint” but rose sharply afterwards. Due to a greater biomass, canola resulted in less runoff than wheat. Scenario analyses showed that increasing crop biomass by 50% under the current average rainfall conditions could reduce runoff by 81‒86% in wheat and 100% in canola. The reduction may be attributed to the combined effects of crop on interception, evapotranspiration, and infiltration. In conclusion, although in a sub-humid continental climate like the Canadian Prairies there are generally low amounts of rainfall runoff, this study demonstrates significant runoff in some years, especially following large rainfall events. Runoff generation can be significantly reduced through improving cropping and tillage practices, and such effects on regional water retention should be further assessed by considering the past and future changes in climate and management.

Intelligent beehive monitoring system based on internet of things and colony state analysis

Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

Formation Mechanism and Slope Stability of the Zhangjiayao Landslide Under Extreme Rainfall Conditions

Formation Mechanism and Slope Stability of the Zhangjiayao Landslide Under Extreme Rainfall Conditions

A coupled regional-scale numerical model for hydrological processes and interactions between groundwater and surface water in a controlled drainage district

Controlled drainage (CD) has emerged as an effective strategy to prevent field waterlogging and mitigate drought during crop growth by altering the hydrological process, while few studies have quantified its effect on groundwater-surface water interactions and groundwater recharge at a regional scale. In this study, a new model is developed for the coupled numerical simulation of water flow in ditches, soil, and underground aquifers under CD conditions. The domain is partitioned into horizontal sub-areas and two vertical layers, with ditches discretized into segments based on groundwater flow grids. The Saint-Venant equations, Richards equation, and MODFLOW are applied to describe water movement processes in ditches, soil, and underground aquifers, respectively. The proposed model is calibrated and validated using five years of observations of ditch water levels (DWL) and groundwater levels, demonstrating excellent agreement with observed data. Subsequently, water balance components of the shallow aquifer below 1 m depth (GW), 1 m of root layer soil profile (SW), and ditch water (DW) under seven scenarios with different control schemes during flood and dry seasons are analyzed to quantify variations in hydrological processes caused by CD. The results show that CD significantly impacts water within SW, GW, and DW, soil evaporation, infiltration, and net recharge from GW to SW (GtoS) by altering regional groundwater table depth (GWT) through the exchange between DW and GW (DtoG and GtoD). GWT and DW show moderate correlation with GtoD, while their correlation with DtoG varies significantly under different rainfall conditions. Smaller precipitation results that precipitation and GWT have a higher linear correlation with water balance components during the dry season compared to the flood season. The correlation values (r) between transpiration (T) and GWT range from −0.99 to 0.96 and −1 to −0.85 during the flood and dry seasons, respectively, indicating a pronounced influence of CD on crop growth. On average, a 1 m increase in DWL control scheme leads to a 0.51 m increase in DWL, and regional GWT decreases by 0.35 m and 0.24 m during flood and dry seasons, respectively. Furthermore, for every 1 m decrease in GWT, net GtoS increases by 36.1 mm and 28.1 mm, respectively, resulting in an increase in SW by 13.9 mm and 11.8 mm, respectively. Considering the varying main crop water stress and the impact of CD under different rainfall conditions, an appropriate CD scheme needs to be adjusted accordingly. These findings provide a quantitative reference for the effect of CD on regional hydrological process and serve as a typical case for similar areas aiming to implement CD strategies for waterlogging and drought control.

Impact of polyvinyl alcohol application and wheat straw mulching on soil loss and infiltration rate in semi-arid tropics

A study was conducted at Punjab Agricultural University, Ludhiana, Punjab, with the aim of monitoring soil loss and infiltration rate in loamy sand soil. The study focused on the effects of applying polyvinyl alcohol (PVA) and mulch under simulated rainfall conditions. The experimental setup involved testing three levels of PVA (0.5%, 0.75%, and 1.0%), one level of wheat-straw mulch (600 g/m2), and a Control treatment (untreated soil). Each of these treatments was replicated four times. The lowest soil loss (20.9 g/m2) was recorded under the 1.0% PVA treatment, while the highest (120.1 g/m2) was seen under the 0.5% PVA treatment. The 1.0% PVA treatment showed a significant reduction in soil loss compared to the 0.5% PVA, 0.75% PVA, mulch, and Control treatments, with reductions of approximately 82.6%, 45.1%, 81.2%, and 89.6%, respectively. Regarding infiltration rates, the Control treatment exhibited the lowest rate (2.4 cm/h), while the 1.0% PVA treatment displayed the highest rate (9.6 cm/h). Additionally, the use of mulch led to a 44.7% reduction in soil loss compared to the treatment without mulch, likely due to the mitigated impact of raindrops. The infiltration rate was significantly higher (4.8 cm/h) under the mulched treatment compared to the unmulched treatment (2.4 cm/h). Overall, the application of PVA and mulch resulted in a drastic reduction in soil loss, likely attributable to the enhanced stability of soil aggregates, improved infiltration rate, and reduced runoff.

An Analytical Insight Into Stability Analysis of Unsaturated Multi‐Layered Slopes Subjected to Rainfall Infiltration

ABSTRACTSlopes in nature usually present layered characteristics, and its stability is susceptible to rainfall events. Considering that current analytical solutions are only suited to simulate the rainfall infiltration of double‐layered infinite unsaturated slopes, an analytical procedure is hence proposed in this study to tackle the consideration of multiple layers. The variable separation method and transfer matrix method are combined to derive the analytical solution of pore water pressure (PWP) for simulating rainfall infiltration in layered infinite unsaturated slopes. After having validated the proposed model and analytical solutions by comparing with existing literature and numerical simulation, the closed‐form solution of PWP is incorporated into the limit equilibrium for assessing slope stability. A three‐layer slope is selected as an example for further discussion. PWP distribution and factor of safety are calculated, considering the effects of saturated hydraulic conductivity and thickness of the upper layer, intensity of antecedent and subsequent rainfall, and varied soil unit weight along the depth. The slope stability subjected to rainfall effects is consistent with the variation of PWP. The proposed analytical solutions provide a simple and practical avenue for computing PWP distribution and evaluating the stability of multi‐layered slopes under rainfall conditions, which can also serve as a benchmark for numerical solutions.

A predictive fuzzy logic and rule-based control approach for practical real-time operation of urban stormwater storage system

Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %∼87 % and 7 %∼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %∼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.

Using demographic modeling to develop post‐fire restoration strategies for a native shrub in a sage scrub community

Mediterranean‐climate shrublands are key biodiversity hotspots and carbon storage pools, but are increasingly threatened by climate change, non‐native species, and altered fire regimes. Fires are important to historic shrubland disturbance cycles but can also promote non‐native plants, which may limit post‐fire native shrub recovery. Increasing drought with climate change could also reduce post‐fire shrub regeneration. I developed a stochastic, individual‐based demographic model (IBM) for the native shrub Artemisia californica, parameterized from an experimental removal of non‐native annuals after a 2013 fire in southern California. The IBM simulated A. californica recovery for 7 years after fire under different rainfall conditions (drought or pre‐drought) and non‐native removal strategies (from no years to all 7 years). Drought lowered A. californica canopy volume 7 years after fire by 90% or more. Rainfall in the second year after fire, when most A. californica germination occurred, had particularly strong effects on final canopy cover. Non‐native removal in all 7 years increased canopy volume by three times under drought conditions and 3.5 times under pre‐drought conditions. Targeting non‐native removal in the first 2 years proved nearly as effective, achieving from 88% (drought) to 95% (pre‐drought) the benefits of removal in all 7 years. In sum, low rainfall may be the most important limitation on post‐fire shrub recovery, but removal of non‐natives in years of pulsed shrub recruitment can be an effective restoration strategy even under drought conditions. More generally, this study illustrates how demographic models can help optimize the targeting of scarce management and restoration resources.

FORAGE YIELD AND QUALITY OF INTERCROPPED ANNUAL RYEGRASS WITH BERSEEM CLOVER SOWN AT DIFFERENT SEED MIXTURE RATIOS UNDER RAINFALL CONDITIONS OF MEDITERRANEAN CLIMATE

This research was carried out under rainfall conditions of Mediterranean climate with the aim to study the effects of different seed mixture ratios on forage yield and quality characteristics of mixture. Annual ryegrass sown as pure produced higher green forage yield than berseem clover sown as pure and the mixtures. In the mixture of berseem clover at 20% seed rate and pure annual ryegrass, higher hay yields were determined compared to the other mixtures. As the seed rate of berseem clover decreased, the proportion of berseem clover in the mixture yield and the crude protein ratio of the herbage decreased linearly. In terms of total LER, BC80-AR20 and BC20-AR80 mixtures were the two most advantageous mixtures, while the aggressivity value of annual ryegrass showed a positive value and became the dominant species. Pure sown berseem clover had lower ADF, NDF and higher crude protein content than pure sown annual ryegrass and other mixtures tested. According to these results, it can be said that berseem clover can be included in the mixture at low seed rates for high forage yield under rainfall conditions, while berseem clover can be grown as pure for high forage quality.

Variation in soil water content and groundwater levels across three land cover types in a floodplain of the Kromme catchment, South Africa

AbstractInvasions of floodplains and riparian areas by alien woody species replacing predominantly herbaceous indigenous vegetation have altered the hydrological and ecosystem functioning in catchments. Although existing studies have examined changes in river flows following the establishment or clearing of alien woody vegetation, our understanding of impacts on soil water content and groundwater remains poor. Limited process knowledge restricts our capacity to reliably model and predict the impacts of land cover changes. As such, this work compared temporal variations in soil water content (SWC) and groundwater levels at three locations with different vegetation types: black wattle (Acacia mearnsii) trees, palmiet (Prionium serratum), and grass (dominated by Pennisetum clandestinum spp), within a floodplain site in the Kromme Catchment in the Eastern Cape Province of South Africa. Soil water content and shallow groundwater levels (&lt; 4 m below ground) were monitored from August 2017 to December 2019 using soil moisture probes and piezometers. Rainfall, vegetation type and antecedent conditions were identified as the major factors controlling observed responses. On average, soil water content and water retention were significantly higher (p &lt; 0.05) at the palmiet site, whilst the wattle site had the lowest SWC among the three sites. Shallow groundwater levels were also higher at the palmiet and grass sites and lowest at the wattle site. Results showed the negative impacts of black wattle trees on SWC and groundwater levels. These results are crucial for improved quantitative predictive capacity which would allow for better catchment management, for example, informing water supply planning and guiding restoration programs focusing on alien plant clearing.

Adjusting Sowing Date and Seeding Rate for Cultivated Lentil to Contrasting Rainfall Conditions in a Semi-arid Region

Adjusting the sowing date and seeding rate of lentils are important practices, which largely affect plant performance and need greater focus under semi-arid conditions. Our study aimed to optimize the sowing window and plant density specific to dry and wet conditions. A two-year field experiment was carried out under dry and wet cropping seasons (2016/2017 and 2017/2018). Two commercial lentil varieties (Syrie229 and Idleb2) were sown at early (late October/early November), normal (December), and late (January) sowing dates and with low (90 seeds/m2), medium (120 seeds/m2) and high (150 seeds/m2) seeding rate. Several traits were recorded namely plant height at first filled pod (HFFP), plant height (PH), days to flowering (DTF), days to maturity (DTM), stem diameter (SD), weight of 100 seeds (100-SW) and seed yield (SY). Our results revealed that the dry year considerably reduced all trait values except the weight of 100 seeds compared to the wet year. Sowing date had a significant effect on most traits, with the highest values being found for early sowing date compared to normal and late dates, including seed yield (1.83 vs. 1.36 and 0.94 ton/ha, respectively). It is worth noting that the difference among sowing dates was detected only under wet years for most traits. The seeding rate affected only HFFP and stem diameter under a wet year, where a high seeding rate increased HFFP while a low seeding rate increased stem diameter. To conclude, cropping season was the main driver of growth and yield, and then the effect of sowing date was dependent on climatic conditions. Our results highlight the importance of adjusting the sowing window and seeding rate to cope with the future climatic change effects.

The Status of Beekeeping in Simada District, Amhara, Ethiopia, with Its Challenges and Opportunities

The study was designed to assess the existing beekeeping practices, constraints, and potential of honeybee production in Simada district. The study was carried out in four proportionally selected kebeles of highland, midland, and lowland agro-ecology. Accordingly, a total sample size of 146 beekeepers, depending on their potential, was interviewed using a structured and semi-structured questionnaire. A semi-structured questionnaire, field observation, and focal group discussion were employed to collect primary data. Descriptive statistics such as mean, frequency, and standard deviation were used to analyze the data. The majority of beekeepers in the district are mail-headed, and the majority can read and write. Beekeepers practiced three hive types but mostly used traditional hives. The majority of honeybee colonies are found in midland agro-ecology, but they are not significantly different (P&amp;lt;0.05). About 57.5% of beekeepers obtain their colony through buying, and their colony increases through reproductive swarming. Beekeepers construct both traditional and top-bar hives from the surrounding available material. Frame hives were obtained from GOs on a credit basis. Beekeepers indicated that the majority of honey was harvested in October and November. The second minor harvesting period was from May to June, which depends on the nature of the yearly rainfall conditions. As the respondent&amp;apos;s described, they stored honey below one year in a plastic jar, clay jar, and plastic sack when plenty of products were obtained and for medicinal value, unless they used honey during harvesting as a source of income. Predators and pests are major constraints on honey bee production, followed by pesticides and herbicides in the study area. Other identified beekeeping constraints were shown in relative order of importance: drought, death of colony, lack of water, migration, and disease are some of the problems that hinder productivity. Honeybees required feed supplementation during the dry season; about 28% of beekeepers fed their colonies with higher supplements made from February to April. The commonly used supplements were peas and bean flour (Shiro), barley flour (Besso), sugar, honey, and others, including Niger.