Variable Rainfall Conditions Research Articles

Space-time prediction of rainfall-induced shallow landslides through Artificial Neural Networks in comparison with the SLIP model

Rainfall-induced shallow landslides are expected to increase due to more intense precipitation linked to climate change. This study aims to develop an effective pixel-based tool for the space-time prediction of soil slips by combining a FeedForward Neural Network (FFN) with insights from the physically-based model SLIP (Shallow Landslide Instability Prediction). The FFN model was developed based on past events in four towns of the Emilia Apennines (Italy) from 2004 to 2014 under varying rainfall conditions. Among the key aspects analysed were the inclusion of both landslide and non-landslide days, the evaluation of two different cumulative rainfall periods (10 and 30 days), and various technical elements related to machine learning, including training approach, network topology, and activation function. A 2:1 imbalance in non-landslide/landslide pixels was implemented to enhance prediction performance. Prediction accuracy was measured using the Quality Combined Index (QCI), which combines AUROC, AUPRC, and F1-score. The best FFN model achieved a QCI of 0.85, accurately predicting non-landslides and minimizing false alarms. A comparison with SLIP showed that SLIP better captured the progressive destabilization in areas nearing instability, while the FFN provided a clearer distinction between stable and unstable zones. A successful blind prediction was demonstrated for a landslide in Compiano (November 2019), validating the model's applicability. SLIP also contributed to understanding the initial soil saturation and rainfall conditions, highlighting its potential to enhance FFN predictions in different meteorological scenarios. Although the developed pixel-based model could be utilized as is, further research is needed to enhance its application for early warning purposes in varying meteorological conditions.

Integrating best management practices with dynamic water environmental capacity for optimal watershed management

Definite setting of water quality target is a challenge when considering non-point source (NPS) pollution which demands reasonable allocation of reduction requirements. However, research on the dynamic water environmental capacity (WEC) allocation aimed at reflecting varying rainfall conditions has rarely been performed. In this study, the relationship between rainfall and WEC was explored by a stochastic algorithm, while then reduction target was allocated in a small catchment, the Three Gorges Reservoir Region (TGRR), China. Combined with the distributed model, three commonly adopted best management practices (BMPs) were located precisely. The results indicate that a specific rainfall threshold exists for water quality (84 mm/month for the study area) which reveals the comprehensive effect of rainfall flushing and dilution effect. At 90% confidence, the total phosphorus (TP) load was 1518 kg/yr, and the ideal WEC was 1194 kg/yr, with excess of 324 kg/yr. In addition, the highest amount of inflow phosphorus can be reduced by fertilizer reduction (FR) with 303.8 kg/yr. Besides, slope-to-terrace (ST) measures should be implemented mainly in the upper reaches, while FR should be preferentially considered in the lower reaches. For the small watershed dominated by agricultural NPS, this study can provide accurate water quality target settled by WEC calculation impacted by uneven rainfall and a feasible watershed management.

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.

Environmental and genetic drivers of physiological and functional traits in a key canopy species

The resilience of forests worldwide is challenged by climate change. Large-scale tree mortality and dieback events have been documented across continents in recent decades. The adaptive capacity of forests is important for predicting forest resistance and resilience to future climates yet remains largely unknown. We grew 12 populations of a widespread foundation tree species (Corymbia calophylla), originating from different temperature and rainfall regimes, in two common garden trials in Western Australia that had similar temperature but contrasting rainfall conditions. We quantified intraspecific trait variation at these two sites to estimate genetically determined trait variation with climate origin (genetic adaptation) and trait variation associated with environment (phenotypic plasticity). We aimed to determine the 1) contribution of genetic and environmental factors on growth, functional, and physiological trait variation; 2) coordination of leaf traits within the context of the leaf economic spectrum (LES) in variable rainfall conditions; and 3) role of local or regional climate adaptation influencing tree growth and water use efficiency. Growth and physiological traits were differentially expressed across populations and sites, highlighting the importance of genetic adaptation and phenotypic plasticity. Leaf traits reflected a more water conservative strategy with higher water use efficiency, high foliar nitrogen content, and low specific leaf area, as predicted by the LES, in trees at the dry site measured in autumn after the warm summer. Local adaptation was detected in growth and leaf water use efficiency traits at the regional climate, not the local population, scale. Plants from the cool region had greater performance than those from the warm region in most plant traits. Home-site rainfall was not a good predictor of trait expression. The capacity of C. calophylla to respond to low water availability through genetic adaptation and phenotypic plasticity may enable it to maintain optimal performance in drier conditions associated with climate change.

Failure pattern of dispersive soil slopes under the action of rainfall conditions

Dispersive soil slopes are widely distributed in Northeast China. To analyze the failure caused by such slopes under various rainfall conditions, section K27 of the road-cutting slope of the Sui-Da Expressway in Heilongjiang Province, China, is considered an example. A 3D slope model was created using the similarity theory and visualized through close-range photogrammetry in a self-made model box. Six model tests were conducted under varying rainfall conditions to investigate the failure modes of dispersive soil slopes. Test results revealed that dispersive soil slopes are subject to two types of failure depending on the rainfall intensity. Slope erosion was divided into three stages by surface random roughness (SRR): initial erosion, constant velocity erosion, and accelerated erosion. Similarity theory suggests that the slope enters the accelerated erosion stage in the actual project when the SRR reaches around 31.95–46.5 cm. Furthermore, the reciprocal of the SRR change rate during the accelerated erosion stage exhibits a linear correlation with time. The time of the slope’s complete failure can be estimated when the reciprocal of the SRR change rate is 0 min/cm. The research findings are significant for comprehending the failure process of dispersive soil slopes.

Functional connectivity related to road linear erosion at rainfall event scale in an agricultural watershed on the Loess Plateau

Intersections of road and channel networks assist in better understanding of runoff and sediment transportation process feedback using the hydrological connectivity concept. However, quantifying functional connectivity and exploring the complex relationship between hydrological connectivity and the processes of runoff and sediment associated with road networks have largely been unexamined by field work. Therefore, our team conducted a 3-year monitoring of hydrological connectivity and road erosion from selected road segments in an agricultural watershed on the Loess Plateau. Rainfall events occurring during 2020–2022 were categorized into three types using K-means clustering analysis: small amounts of low-intensity rainfall (SL-R), large amounts of low-intensity rainfall (LL-R), and large amounts of high-intensity rainfall (LH-R). The monitored roads primarily consist of paved roads and unpaved roads (agricultural roads, abandoned roads, and petroleum roads). In our study, the intersections extracted by the road and channel networks were defined as hydrological connections, and sticks wrapped with clean gauze at the hydrological connections were monitored under variable rainfall conditions for functional connectivity assessment. The runoff heights and sediment traces attached to the sticks are alternative hydrological connectivity indicators. The results revealed an obvious increasing trend in the activation frequency and degree of hydrological connectivity as rainfall types transitioned from SL-R to LL-R and LH-R. Agricultural roads had a more intense response to hydrological connectivity between upslope drainage areas and road surfaces, and petroleum roads performed well in response to hydrological connectivity between road surfaces and down hillslopes. Random forest models indicate that PI30 is the most dominant influencing factor of road erosion and hydrological connectivity between road upslope drainage areas and road surfaces. Road surface erosion rates are the primary determinants of hydrological connectivity between road surfaces and down hillslopes. These findings suggest a close interaction between road erosion and hydrological connectivity driven by these off-site effects. They offer additional evidence concerning runoff and sediment patterns and dynamics in relation to road networks and identify hydrological connectivity and road erosion hotspots. Coordinating measures for hydrological connectivity regulation and road erosion mitigation are essential for minimizing hydrological connectivity and road erosion off-site effects within watersheds.

Simulating the Responses of Rice Yield and Nitrogen Fates to the Ground Cover Rice Production System under Different Precipitation Year Types

The ground cover rice production system (GCRPS) has considerable potential for securing rice production in hilly mountainous areas. However, its impact on yields and N fates remains uncertain under varying rainfall conditions. A two-year field experiment (2021–2022) was carried out in Ziyang, Sichuan Province, located in the hilly mountains of southwest China. The experiment included two cultivation methods: conventional flooding paddy (Paddy, W1) and GCRPS (W2). These methods were combined with three N management practices: N1, no-N fertilizer; N2, 135 kg urea N per hm2 as a base fertilizer; and N3, 135 kg urea N per hm2 with split application for W1 and 67.5 kg N per hm2 of urea and chicken manure separately for W2. The WHCNS (soil water heat carbon nitrogen simulator) model was calibrated and validated to simulate ponding water depth, soil water storage, soil mineral N content, leaf area index, aboveground dry matter, crop N uptake, and rice yield. Subsequently, this model was used to simulate the responses of rice yield and N fates to GCRPS under different precipitation year types using meteorological data from 1980 to 2018. The results indicated that the WHCNS model performed well in simulating crop growth and N fates for both Paddy and GCRPS. Compared with Paddy, GCRPS reduced N leaching (35.1%–54.9%), ammonia volatilization (0.7%–13.6%), N runoff (71.1%–83.5%), denitrification (3.8%–6.7%), and total N loss (33.8%–56.9%) for all precipitation year types. However, GCRPS reduced crop N uptake and yield during wet years, while increasing crop N uptake and yield during dry and normal years. Fertilizer application reduced the stability and sustainability of rice yield in wet years, but increased the stability and sustainability of rice yield in dry and normal years. In conclusion, GCRPS is more suitable for normal and dry years in the study region with increasing rice yield and reducing N loss.

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Numerical Modelling of Matric Suction in Unsaturated Soil under Shallow Foundation Under Varying Soil and Hydrological Conditions

In recent times, extreme hydrological events have disrupted the performance of various structures, particularly the structural foundations responsible for transferring the superstructure's weight to the natural ground. This disruption underscores the significance of matric suction and soil saturation, which are influenced by hydrological conditions like precipitation, soil shear strength, and foundation settlement. These factors are essential when designing structures in specific locations with distinct geotechnical parameters. To address these challenges, our research employs Plaxis 2D numerical modeling to investigate the dynamic changes in matric suction within soil beneath shallow foundations under varying rainfall conditions. Our approach involves a fully-coupled flow method, incorporating the Van Genuchten hydraulic model. In recognition of practical constraints, we utilize fundamental soil classification parameters for model configuration. Our findings reveal the substantial impact of matric suction fluctuations during rainfall on soil deformation, as indicated by displacement patterns. This highlights the critical importance of matric suction in comprehending soil behavior. Furthermore, we observe that higher initial water table levels correlate with reduced variations in matric suction and soil deformation during rainfall, emphasizing the regulatory role of water table depth. In conclusion, this study emphasizes the necessity of considering matric suction and water table depth in structural design and geotechnical analysis, particularly when faced with extreme hydrological events. By comprehending these factors, we can enhance our understanding of soil behavior, improve foundation stability, and develop more effective design strategies for structures in various environmental conditions.

Assessing the effectiveness of site real-time adaptive control for stormwater quality control

Urban sprawl, excessive rainfall, and resource limitations have challenged the performance of conventional water-sensitive urban designs (WSUD). To address these issues, there is a growing interest in adopting site real-time adaptive control (SRAC) for integrated and real-time water management. This research evaluates the effectiveness of SRAC technology in reducing stormwater pollutants in a highly developed urban area, comparing it with conventional WSUD methods. The study focuses on examining the impact of the SRAC-WSUD method on stormwater quality. Findings indicate that the SRAC-WSUD method reliably reduces stormwater pollutants under varying rainfall conditions. Moreover, it enhances the resilience and performance of existing stormwater systems without the need for additional infrastructure upgrades. For the whole year scenario, the SRAC-WSUD method resulted in reductions in total pollutant loads. Notably, there was a 196.72 kg/year decrease in total suspended solids (TSS), a 0.07 kg/year decrease in total phosphorus (TP), and a 0.78 kg/year decrease in total nitrogen (TN). These findings highlight the potential of SRAC-WSUD in improving stormwater quality and its implications for sustainable urban water management. Overall, this research contributes to advancing the understanding and practical implementation of SRAC technology in stormwater management, offering valuable insights for urban planning and water resource preservation.

Analysis of Slope Stability with Different Vegetation Types under the Influence of Rainfall

Rainfall-prone shallow landslides account for one-fifth of the global land area, and rainfall is critical to the mechanics and hydrology of shallow slopes. In typical geological disaster-prone areas, the hydrodynamic responses of slopes with different vegetation types under rainfall conditions require further study. The purpose of this study was to analyze the hydraulic stability of soils with different vegetation types under rainfall conditions and their effects on slope stability. Thus, the soil–water characteristic curves and water-stable aggregate characteristics of soils with three vegetation types were analyzed. A two-dimensional finite element model was used to simulate the slope stability of extreme rainfall environments with different rainfall durations. The results showed that the matric suction of soil with trees was less affected by rainfall with a better stability of water-stable aggregates than that of soil with shrubs and grass. The plastic strain cloud map showed that the maximum plastic strain occurred at the toe of the slope. In addition, the potential slip depth of slopes with trees was smaller than that of slopes with shrubs and grass. Under the two rainfall durations, the factor of safety (FoS) of slopes with trees changed by 0.06, whereas that of slopes with shrubs and grass changed by 0.1. The findings of this study provide valuable insights into changes in the stability of slopes with different vegetation types under varying rainfall conditions. It is of great significance to provide a scientific basis for the application of ecological measures in the prevention and control of mountain disasters and guide the implementation of appropriate land management measures.

Functional diversity buffers biomass production across variable rainfall conditions through different processes above‐ versus below‐ground

Abstract Understanding precipitation controls on functional diversity is important in predicting how change in rainfall patterns will alter plant productivity in the future. Trait‐based approaches can provide predictive knowledge about how certain species will behave and interact with the community. However, how functional diversity relates to above‐ and below‐ground biomass production in variable rainfall conditions remains unclear. Here, we tested the role of mass ratio and niche complementarity hypotheses in shaping above‐ and below‐ground biomass–functional diversity relationships in seasonal drought. We implemented a fully crossed experiment that manipulated drought timing (fall dry, spring dry, consistent dry and ambient rainfall) and community composition (grass‐dominated, forb‐dominated and mixed grass–forb) in a California annual grassland. Plant communities with mixed functional groups showed higher above‐ and below‐ground biomass than either the grass‐ or forb‐dominant communities. We found divergent functional diversity–biomass relationships for above‐ and below‐ground biomass. Above‐ground biomass decreased with community weighted means (CWMs) of specific leaf area (SLA) and height, supporting the mass ratio hypothesis, which posits that dominant species with specific traits drive biomass production of the community. Below‐ground biomass showed no evidence of either mass ratio hypothesis or niche complementarity. While biomass was largely unaffected by the timing of drought in one season, we found community‐wide functional trait shifts in response to rainfall treatments. Above‐ground traits shifted to higher SLA in consistent dry compared to ambient. Below‐ground traits shifted to longer, finer and denser roots in fall and consistent dry, and shorter and coarser roots in spring dry. Functional diversity buffered biomass production by enabling shifts in above‐ and below‐ground functional traits across variable rainfall conditions. Read the free Plain Language Summary for this article on the Journal blog.

Incentivizing sustainable fire management in Australia's northern arid spinifex grasslands

Fire management across Australia's fire-prone 1.2 M km2 northern savannas region has been transformed over the past decade supported by the inception of Australia's national regulated emissions reduction market in 2012. Today, incentivised fire management is undertaken over a quarter of that entire region, providing a range of socio-cultural, environmental, and economic benefits, including for remote Indigenous (Aboriginal and Torres Strait Islander) communities and enterprises. Building on those advances, here we explore the emissions abatement potential for expanding incentivised fire management opportunities to include a contiguous fire-prone region, extending to monsoonal but annually lower (<600 mm) and more variable rainfall conditions, supporting predominantly shrubby spinifex (Triodia) hummock grasslands characteristic of much of Australia's deserts and semi-arid rangelands. Adapting a standard methodological approach applied previously for assessing savanna emissions parameters, we first describe fire regime and associated climatic attributes for a proposed ∼850,000 km2 lower rainfall (600–350 mm MAR) focal region. Second, based on regional field assessments of seasonal fuel accumulation, combustion, burnt area patchiness, and accountable methane and nitrous oxide Emission Factor parameters, we find that significant emissions abatement is feasible for regional hummock grasslands. This applies specifically for more frequently burnt sites under higher rainfall conditions if substantial early dry season prescribed fire management is undertaken resulting in marked reduction in late dry season wildfires. The proposed Northern Arid Zone (NAZ) focal envelope is substantially under Indigenous land ownership and management, and in addition to reducing emissions impacts associated with recurrent extensive wildfires, development of commercial landscape-scale fire management opportunities would significantly support social, cultural and biodiversity management aspirations as promoted by Indigenous landowners. Combined with existing regulated savanna fire management regions, inclusion of the NAZ under existing legislated abatement methodologies would effectively provide incentivised fire management covering a quarter of Australia's landmass. This could complement an allied (non-carbon) accredited method valuing combined social, cultural and biodiversity outcomes from enhanced fire management of hummock grasslands. Although the management approach has potential application to other international fire-prone savanna grasslands, caution is required to ensure that such practice does not result in irreversible woody encroachment and undesirable habitat change.

Bartlett–Lewis Model Calibrated with Satellite-Derived Precipitation Data to Estimate Daily Peak 15 Min Rainfall Intensity

Temporal variability of rainfall is extreme in the rangelands of northern Australia and occurs at annual, decadal, and even longer timescales. To maintain long-term productivity of the rangelands of northern Australia under highly variable rainfall conditions, suitable land management practices are assessed using rangeland biophysical models, e.g., GRASP (GRASs Production). The daily maxima of the 15 min rainfall intensity (I15) are used to predict runoff and moisture retention in the model. The performance of rangeland biophysical models heavily relies on the I15 estimates. As the number of pluviograph stations is very limited in northern Australian rangelands, an empirical I15 model (Fraser) was developed using readily available daily climate variables, i.e., daily rainfall total, daily diurnal temperature range, and daily minimum temperature. The aim of this study is to estimate I15 from daily rainfall totals using a well-established disaggregation scheme coupled with the Bartlett–Lewis rectangular pulse (BLRP) model. In the absence of pluviograph data, the BLRP models (RBL-E and RBL-G) were calibrated with the precipitation statistics estimated using the Integrated Multi-satellitE Retrievals for GPM (global precipitation measurement) (IMERG; 30 min, 0.1° resolution) precipitation product. The Fraser, RBL-E, and RBL-G models were assessed using 1 min pluviograph data at a single test site in Darwin. The results indicated that all three models tended to underestimate the observed I15, while a serious underestimation was observed for RBL-E and RBL-G. The underestimation by the Fraser, RBL-E, and RBL-G models consisted of 23%, 38%, and 50% on average, respectively. Furthermore, the Fraser model represented 29% of the variation in observed I15, whereas RBL-E and RBL-G represented only 7% and 11% of the variation, respectively. A comparison of RBL-E and RBL-G suggested that the difference in the spatial scales of IMERG and pluviograph data needs to be addressed to improve the performance of RBL-E and RBL-G. Overall, the findings of this study demonstrate that the BLRP model calibrated with IMERG statistics has the potential for estimating I15 for the GRASP biophysical model once the scale difference between IMERG and point rainfall data is addressed.

Estimation of groundwater recharge in semiarid regions under variable land use and rainfall conditions: A case study of Rajasthan, India

In the semiarid regions of India, the annual rainfall is very low (~650 mm) and erratic; hence groundwater recharge is vital to support crops, especially in the winter season. For groundwater budgeting it is essential to consider how groundwater recharge is affected by both land-use and rainfall distribution. This study used a soil water balance approach, considering hydrological, meteorological, hydrogeological and crop information to understand the recharge process in semiarid regions. The approach was used at a sub-watershed scale where farmers grow rainfed and irrigated crops. Delayed recharge response on the water table was considered to estimate actual recharge, which closely matches the observed water levels in the field. The recharge estimated in rainfed agricultural lands, rainfed-irrigated agricultural lands, and barren lands was 29%, 17%, and 31% of the total inflow.

Three-dimensional stability analysis of unsaturated slopes under variable rainfall conditions using a numerical method

Three-dimensional stability analysis of unsaturated slopes under variable rainfall conditions using a numerical method

Fodder Grass Strips: An Affordable Technology for Sustainable Rainfed Agriculture in India

Rainfed agriculture, though resource-poor, contributes to around 40 percent of total food production in India. Fodder grass-strip-based systems improve soil’s physical and biological properties, control soil erosion, and help in slope stabilization without compromising productivity. Permanent fodder grass strips can effectively check the depletion of soil nutrients and can also act as sediment traps vis-à-vis meeting the green fodder requirement for small ruminants. This study was carried out with the major objective to quantify the impact of grass-strip-based cropping systems on soil quality. Further fodder quality assessment was carried out using the grass quality index for small ruminant feed and the profitability of different treatments was analyzed. Random block design (RBD) with three treatments which included two types of fodder grass (Brachiaria ruziziensis and Stylosanthes hamata) on both sides of the cropped field was used for the study. The results showed that the soil quality increased from 0.39 to 0.52 and the runoff reduced significantly with soil loss reduction by 65-70 percent. The fodder quality assessment showed that the palatability of Stylosanthes hamata and Brachiaria ruziziensis was about 65 percent and 40 percent, respectively. The fodder grass strip increased the net returns by 30 percent. This easily adaptable natural resource management technology reduces soil nutrient loss and will help resource-poor rainfed farmers to maintain soil health and productivity under variable rainfall conditions with fair support to small ruminants.

Modeling sorghum-cowpea intercropping for a site in the savannah zone of Mali: Strengths and weaknesses of the Stics model

Intercropping is a key entry point for sustainable intensification of cropping systems in sub-Saharan Africa where variable rainfall conditions prevail. Crop simulation models can complement field experiments to assess the agronomic and environmental performances of intercropping systems under diverse climatic conditions, including hypothetical future climate. So far, crop models that can handle intercropping, such as STICS, have not often been extensively evaluated for tropical conditions and for species grown by farmers in sub-Saharan Africa. The objective of this study was to evaluate the performance of the calibrated STICS model to simulate sorghum-cowpea intercropping systems in rainfed conditions in West Africa. We used data from field experiments conducted at the N'Tarla Agronomic Station in Mali in 2017 and 2018. Two varieties of sorghum (local and improved) with contrasting photoperiod sensitivities were grown as sole crop and intercropped with cowpea, with additive design. Two sowing dates and two levels of mineral fertilization were also investigated. Model simulations were evaluated with observed data for phenology, leaf area index (LAI), aboveground biomass, grain yield and in-season soil moisture. Large variations in aboveground biomass of sorghum and cowpea was observed in the experiment (i.e. 3.5 – 9.6 t/ha for sorghum and 0.4–2.5 t/ha for cowpea), owing to the treatments (i.e. sole vs intercrop, early vs late sowing, no fertilizer input vs fertilizer input). Such variations were satisfactorily reproduced by the model, with EF of 0.81 in calibration and 0.58 in evaluation (with relative rRMSE of 23 % and 43 %) across crops. Sorghum AGB simulations were more accurate (rRMSE of 21 % and EF of 0.54) than cowpea AGB simulations (rRMSE of 25 % and EF of −0.09). The two main observed features of the intercropping system were well reproduced by the model. Firstly, cowpea and sorghum aboveground biomass decreased with intercropping compared with sole cropping, and the decrease in cowpea biomass was greater than the decrease in sorghum biomass. Secondly, despite a reduction in sorghum and cowpea yield, Land Equivalent Ratio of the intercropping for aboveground biomass was always above one. With regard to grain yield, observed LER was above one only in the non-fertilized treatment. The model failed at reproducing this behavior, probably because of insufficiently accurate calibration of the process leading to grain yield formation: rRMSE for grain yield was 49 % in calibration and 41 % in evaluation. Based on these findings, we discuss avenues to improve model calibration and use the model to explore options for sustainable intensification in land constrained sub-Saharan Africa. • Accurate STICS simulations of intercropped sorghum and cowpea aboveground biomass. • Inaccurate STICS simulations of intercropped sorghum and cowpea grain yield. • The model can be used to start exploring inter-annual performance of intercropping. • Experiments looking at N dynamics in intercropping needed to refine calibration.

Forest cover lessens the impact of drought on streamflow in Puerto Rico

AbstractTropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15‐min streamflow data collected between 2005 and 2016 from 20 US Geological Survey stream gages and 3‐hourly Multi‐Source Weighted‐Ensemble Precipitation rainfall estimates, we utilized flow‐duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.

Genetic variability and association of yield and yield components among bread wheat genotypes under drought-stressed conditions

Drought is one of the major constraints to wheat production and productivity globally. Developing drought-adapted wheat cultivars is paramount to increase wheat productivity under variable rainfall conditions. Understanding the genetic variability and trait association is key to the development of improved wheat cultivars. The objective of this study was to determine the extent of the genetic parameters and associations of yield and yield components of bread wheat genotypes, in order to design appropriate breeding strategies for yield improvement in wheat. One hundred and twenty genotypes were evaluated at five test sites in the 2018/19 cropping season using a 10 x 12 alpha lattice design with two replications. Different sowing dates were used to impose contrasting drought stress levels based on the onset of the main seasonal rains at each site. Data were recorded on agronomic traits such as days to heading (DH), days to maturity (DM), plant height (PH), spike length (SL), spikelet per spike (SS), kernel per spike (KS), 1000 kernel weight (TKW) and grain yield (GY). There was significant (p&lt;0.01) genetic variation for all agronomic traits studied under both drought-stressed and non-stressed conditions. The highest estimates for genetic variance were obtained for DH (54.0%), followed by SL (38.3%). The high heritability estimated for DH (94.4%), SL (90.2%) and SS (85.2%), coupled with a high rate of genetic advance, suggest that direct selection for these traits would be effective under drought-stressed conditions. GY exhibited low genetic advance (9%) and heritability (41.5%) estimates, which were concomitant with its polygenic and complex inheritance pattern. Correlation and path analyses revealed that TKW was the most important contributing trait for improving grain yield under drought-stressed conditions