Maximum Soil Moisture Research Articles

Tree carbon stock and seasonal influence on soil properties in shivpuri-nagarjun national park, nepal

The carbon stock stored within the biomass of tree species is vital in the forest ecosystem as it contributes significantly to the carbon balance. In addition, the physicochemical properties of soil play a critical role, in influencing overall ecosystem health. This study has analyzed the carbon stock and influence of seasonal change on soil physicochemical properties along soil depths in the Shivpuri Nagarjun National Park (SNNP), Nepal. The above-ground biomass carbon stock was found to be 227.09 t/ha and below-ground stock was 45.42 t/ha. Tree species Castanopsis tribuloides exhibited the highest values of above and below-ground tree carbon stock. The soil of the study site was sandy loam and slightly acidic. High temperature and moisture in the monsoon season were followed by an increased bulk density during the pre-monsoon with deeper soil layers. The sand, silt, and clay contents did not differ significantly across the seasons and depths. The key soil nutrients, like carbon, total nitrogen, phosphorus, and potassium were high during the monsoon season at the topsoil layer, which gradually declined with increasing depth in all seasons. The study highlights that the total tree carbon stock in the study site is 272.51 t/ha, with significant seasonal and depth-related variations in soil attributes. The monsoon season, characterized by maximum soil moisture and higher concentrations of essential soil components, is crucial in influencing soil physicochemical properties and offers important insights for forest conservation and management. Bangladesh J. Bot. 53(3): 567-576, 2024 (September)

Assessment of land configuration techniques on in-situ soil moisture conservation and productivity of rainfed Bt. Cotton in semi-arid production systems

Rainfed agriculture is proving to be a significant contributor to cotton production. However, recent climate change characterizes a substantial threat to current rainfed agricultural production, and consequently to farmers’ livelihoods in semi-arid regions. Typical features of these areas are erratic rainfall to frequent dry spells that lead to frequent crop failure and lasting poverty. In-situ soil moisture conservation is an important climate resilience agricultural practice to enhance the productivity of crops in these regions including rainfed Bt cotton. It comprises agronomical land configuration techniques with several local modifications. The experiments were conducted in a field at Okkarai village (11o 22’ N latitude and 78o 53’ E longitudes), Thuraiyur Block, Tiruchirapallai District in the Northwestern Agro-climatic Zone of Tamil Nadu. It was undertaken during the Rabi season (North-East monsoon) of 2022 and 2023. The experiments were laid out in randomized block design (RBD) with 7 treatments with three replications. The treatments consist of different land configuration techniques viz., flat-bed, ridges and furrows, broad bed and furrows (BBF), raised bed, tied ridges, tied ridges (alternate rows) and tied broad beds (TBB). An analysis of pooled mean data of two years showed significant differences between the treatments. Among the land configuration techniques tested, tied ridges (alternate rows) registered the maximum soil moisture content during flowering and boll formation (90 DAS), boll development (120 DAS) and boll and fibre maturation (140 DAS) stages, and the increase was 34.4%, 31.8% and 15.74%, respectively over flat-bed method. Likewise, tied ridges (alternate rows) significantly enhanced the cotton growth characters viz., plant height (128.92 cm), leaf area index (4.52), dry matter production (5.19 t/ha), and yield attributes viz., number of squares (44.50/plant), number of bolls (43.73/plant), boll weight (4.76 g) and seed cotton yield (20.86 q/ha) and cost-benefit ratio (3.07). Tied ridges in alternative row served as low-cost micro catchment approach for trapping and holding water and its practice in semi-arid rainfed region has beneficial effects of reducing runoff and soil loss, increasing soil moisture and augment crop yield.

Estimation of water inflows to reservoirs of irrigation dams in the Guinea Savannah ecological zone of Ghana

ABSTRACT The study was carried out in the northern Ghana to estimate water inflows to nine reservoirs of irrigation dams for a period of 20 years (1999–2018) using the USDA Natural Resources Conservation Service Curve Number model. The key input parameters of the model were hydrologic soil group, rainfall amount, landuse/landcover, weighted curve number, antecedent moisture condition, and potential maximum soil moisture retention. The annual rainfall within the reservoir catchments was found to vary between 617.20 and 1,382.30 mm. The estimated annual runoff depths ranged from 54.10 to 125.55 mm. A high degree of positive correlation was found between rainfall and runoff depths with a coefficient of determination (R2) ranging from 79.80 to 90.70%. Hydrologic soil groups accounted for about 67.2 and 62.5% of the variation in runoff depth and percentage runoff, respectively. The estimated annual water inflows to the reservoirs ranged from 145,206 ± 125,814 m3/y at Gambibgo to 47,074,634 ± 4,395,860 m3/y at Tono. The variation in the annual water inflows and annual storage capacity recharge among the various reservoirs was noted to be influenced by reservoir size, catchment size, rainfall, and soil characteristics in the reservoir catchments.

Straw Use to Reducing Soil Compaction and Its Effect on the Biometric and Physiological Characteristics of Soybean and Maize Plants

ABSTRACT The objective of this study was to evaluate the straw to decrease the bulk density of sandy clay loam and clay soils and to determine the effect of this on the development of soybean and maize plants. The soils were compacted using Proctor test with different amounts of crushed straw on the soil surface corresponding to 0, 3, 6, 9 and 12 t ha−1. After this, the soils in pots were compacted with same levels of straw on its surface using the maximum bulk density each soil. Then, soybean and maize were sown and after 42 days, biometric characteristics and physiological parameters were evaluated. The results showed that the maximum bulk density and critical soil moisture for the sandy clay loam soil were 1.51 t m−3 and 0.15 kg kg−1, respectively, while those for the clay soil were 1.54 t m−3 and 0.19 kg kg−1. The straw reduced soil compaction for the two soils, resulting in lower soil bulk density with 9 and 8 t ha−1 of straw on the surface of sandy clay loam and clay soils, respectively. When evaluated the plant, the amounts 6 to 9 t ha−1 of straw promoted better initial development of soybean and maize plants. Thus, this study suggests that a minimum of 6 and 8 t ha−1 of straw be used on the surface of sandy clay loam and clay soils, respectively were efficient in promoting physical soil improvements and better plant development.

Response time of soil moisture to rain in a vineyard with permanent cover

The time elapsed between the moments of maximum rainfall intensity and maximum soil moisture, known as peak to peak (P2P), is part of the hydrological response of the soil, but literature has missed this metric in any woody crop. In a vineyard with permanent vegetation cover (humid climatic conditions), the influence of two cultivars (Agudelo and Blanco Legítimo) and two zones (rows and inter-row areas) on the values of soil moisture response time, absolute change in moisture (ΔS) and P2P was evaluated for 118 rainfall events in three soil layers. 12 capacitance probes and a weather station were used, and data measured every 15 min. A positive response (ΔS > 0) was observed in 79 %, 73 % and 67 % of all events at soil depth of 5, 15 and 25 cm, decreasing ΔS with increasing soil depth. Differences of ΔS were significant among layers, but not among cultivars and zones. The maximum ΔS occurred at 15 cm, while the minimum was observed at 25 cm. No response was evident when specific thresholds were not reached: rainfall depth (0.60 mm event−1), maximum intensity (1.20 mm h−1) or duration (30 min). Topsoil conditions –high rainfall interception by the dense cover and high soil organic matter content– influenced the results at 5 cm. Rain parameters correlated well with ΔS, but weak with the response time and P2P. P2P occurred earlier in the rows than between the rows, especially at 15 cm. Shorter P2P appeared in Agudelo, with significant differences in the rows at 5 cm. P2P differed significantly among layers, increasing P2P with soil depth. Similar ΔS values appeared in Spring and Autumn, and were significantly different than those in Summer, but P2P did not differ significantly among seasons. Therefore, the magnitude and timing of the soil hydrological response were independent processes.

Increasing risk of synchronous floods in the Yangtze River basin from the shift in flood timing

Floods are some of the most frequent and severe natural hazards worldwide. In the context of climate change, the risk of extreme floods is expected to increase in the future. While, the trends in flood timing and risk for flood synchronization remain unclear. In this study, the seasonality of flood peaks, annual maximum rainfall, and annual maximum soil moisture in the Yangtze River Basin were examined using observational and reanalysis data from 1949 to 2020. Changes in the timing of extreme events may increase the possibility of concurrent flooding, therefore the risk for synchronous floods were further explored. The results indicate that the seasonality of floods has a strong consistency with that of annual maximum rainfall. In the southern Yangtze River Basin, floods usually occur between early June and early July, with a delayed trend. However, they occur slightly later in the north, generally from late July to early August, with a tendency of advance. Overall, the timing of floods is positively correlated with rainfall and soil moisture peaks, and the correlation is much stronger for annual maximum rainfall. However, for more intense floods or for larger catchments, soil moisture plays an important role in modulating the variations in flood timing. Reverse latitudinal changes in flood timing are expected to result in more synchronous floods. The synchrony frequency exceeded 60 % for most of the stations, and the frequency was increasing for nearly half of the region, especially in the middle reaches, Poyang Lake and south of Dongting Lake. In addition, the flood synchrony scale in the south of the basin showed significant upward trends. These findings would provide important implications for flood risk management and adaptive strategy development.

Flooding in the Yellow River Basin, China: Spatiotemporal patterns, drivers and future tendency

Study regionThe Yellow River Basin (YRB) in China. Study focusThis study is devoted to simulating flood changes with return periods of 10, 20, 30, 50, 100 and 200 years under three Representative Concentration Pathways (RCPs) using CaMa-Flood models, and examining the drivers. New hydrological insights for the regionIn the historical period (1961–2005), the middle and lower YRB and specifically the Wei River Basin (WRB) (a tributary of the middle YRB) are dominated by higher flood risks with once-in-200-year flood magnitude of 9989.63 m3/s. Under climate change scenarios, the relative change in the once-in-100-year flood magnitude increased from 125.0% (RCP2.6) to 204.9% (RCP8.5) during the historical period (1961–2005) to the long-term period (2076–2092). The historical flood-prone regions were more susceptible to increased flood magnitude. Meanwhile, the area with increased flood risks ranged from 73.6% to 95.2% in the YRB. Increased flood magnitude was typically related to lengthened return interval of floods under the RCP2.6 and RCP6.0 scenarios, while decreased flood magnitude was commonly linked to increased return intervals under the RCP8.5 scenarios. Notably, we observed higher correlations between 7-day maximum precipitation and floods compared to those between daily maximum precipitation, soil moisture, and floods. Co-occurrences between flood events and hydrometeorological extremes were higher in the upper and middle YRB.

TILLAGE PRACTICES AFFECT THE WATER CONSERVATION BENEFITS OF RAINWATER HARVESTING IN SEMI-ARID CONDITIONS

Livelihood in the Pothwar region of Pakistan is largely dependent on rainwater. Therefore, storing and conserving rainwater for its subsequent use when required is crucial. Amongst various means of in-field moisture conservation, tillage practices play a dynamic role, especially in loose soils of Pothwar area. Under this study, the efficiency of commonly used tillage implements i.e., mould board (MB) plough, disc plough and cultivator, was evaluated for moisture conservation and improvement in wheat (Triticum aestivum L) yield. The three years’ study showed that maximum soil moisture and grain yield were obtained in the field tilled with MB plough followed by disc plough. It has been observed that soil water was improved by 11.3, 10.6, 9.9 and 11.5 % in the treatment of MB plough, while this increase was recorded as 5.7, 9.0, 5.2 and 6.8 % in disc plough as compared to cultivator at sowing, 02 months & 04 months after sowing and harvesting stage, respectively. In the treatment of MB plough, improvements were observed as a wheat grain yield 16%, wheat straw yield 19%, productive tillers 6% and plant height by 2%. Furthermore, MB plough enhanced its productivity and profitability with the highest benefit-cost ratio of 1.83. The role of disc plough in soil water conservation and crop productivity enhancement was observed less than MB plough. The current study observed that use of MB plough is beneficial in soil water conservation and improvement of crop yield.

Tillage Practices Affect Rhizospheric Moisture Availability and Performance of Chickpea (Cicer arietinum L.) Cultivars under Spate Irrigation in Punjab, Pakistan

Selection of suitable tillage technique can ensure most productive use of available hill torrent water in spate irrigated areas for improved crop productivity and profitability. During rabi season 2021-22, a field study on chickpea was undertaken in Vidor hill torrent command area of Dera Ghazi Khan Punjab, Pakistan. In this experiment three cultivars of chickpea viz Bittle-2016, Thal-2020 and Bhakhar-2011 were evaluated under spate irrigated conditions along with local cultivar (Farmer practice) for comparison. Tillage practices at the seedbed preparation stage evaluated were cultivator and rotavator as follows i.e. T1= Two turns of cultivator (Farmer practice), T2=Three turns of cultivator, T3=Two turns of cultivator + one turn of rotavator, T4= one turn of cultivator + one turn of rotavator. The experiment was conducted under randomized complete block design (RCBD) with factorial arrangement and treatments were replicated three times. Soil moisture was measured through gravimetric method and chickpea yield parameters were estimated as per standard procedures. The means were tested for significance using HSD Tuckey’s test at 5 % level of probability. Results revealed that cultivars and tillage practices showed significant variations for tested parameters. In this experiment maximum soil moisture conservation, number of branches per plant, pods per plant, seeds per pod, seed yield (kg ha-1) and harvest index (%) were observed from Farmer practice i.e. 2 turns of cultivator under local cultivar used. Moreover, comparatively similar results were revealed from the plots where 3 turns of cultivator was run and Thal-2020 cultivar was grown under the local conditions of Vidor hill torrent command area of Dera Ghazi Khan, Punjab, Pakistan. Plots where 2 turns cultivator was used, the maximum benefit cost ratio (BCR, 1.85) was observed from local cultivar (Farmer practice). Moreover, among newly tested cultivars, Thal-2020 showed encouraging results in terms of soil moisture contents, yield attributes with BCR 1.73 in plots where 2 turns cultivator was run. Under the prevailing agro normals of spate irrigated conditions of Vidor hill torrent command area, farmers should use 2 turns of cultivator with local cultivar. However, more research is needed to acclimatize latest varieties of chickpea after adaptability trials.

Sensitivity Analysis of the Land Surface Characteristic Parameters in Different Climatic Regions of the Loess Plateau

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

기상 및 토양환경 정보를 이용한 노지 채소의 토양수분 변화 및 예측 모델 개발

Soil moisture is an essential element for plant growth and is biochemically affected by weather and soil environments. Farmers have difficulties in managing soil moisture, and existing soil moisture prediction models require a lot of environmental information. Therefore, we intend to develop a model that predicts soil moisture per hour using only limited environmental information. First, the relationship between soil moisture and the environment is identified through correlation analysis. Next, a statistical soil moisture prediction model was developed through a nonlinear regression model (NLS) using a sigmoid function. Finally, in order to confirm whether the proposed model is suitable as a soil moisture prediction model, it was compared with the machine learning algorithms of support vector regression (SVR), random forest (RF), and extreme gradient boosting (XGB), which are widely used in previous studies. Through root mean square error (RMSE) and mean absolute error (MAE), it was found that the NLS model was superior in field cultivation and underestimated the maximum soil moisture in paddy cultivation compared to other models. Since the proposed model is easy to apply in various soil environments, it is expected to help as a useful soil moisture management system for farmers.

Functional urban ground-cover plants: identifying traits that promote rainwater retention and dissipation

Urban vegetation can influence urban hydrology and reduce the risk of flooding. Urban forestry studies have suggested that tree type and species choice affect the amount of rainwater intercepted and retained. Little information exists, however, for other landscape typologies, and the sorts of ground-cover plants that are best used to retain/detain rainwater during storm events. This is important as many urban spaces are too small to facilitate trees, but can accommodate roadside vegetation, buffer strips, rain gardens, green roofs and stormwater planters. Thus, this research aimed to determine how choice of ground-cover taxa affected rainwater interception and retention. Six model species with contrasting leaf morphologies were used to determine how well rainwater was intercepted, but also dissipated through evapotranspiration (ET). A pot-based system was used to determine how plant water balance changed during late summer in the UK, with the aim to understand how leaf traits affected hydrological processes. Plant choice was important, with fine-leaved taxa, Festuca glauca and Dianthus ‘Haytor White’ showing best rainwater interception and Festuca demonstrating highest rates of dissipation from the substrate. Overall, compared to non-planted pots, those with plants present were more effective at capturing water (by 2.3–3.0x), and evapo-transpiring water (by 2.5-4.0x). Results indicate that ground cover vegetation has potential to aid urban water management in those localities where space is limited for trees. Plant choice and community-structure should be considered, especially when there is a desire to dry out soil/substrate quickly and restore maximum soil moisture holding capacity.

Afforestation reduced the deep profile soil water sustainability on the semiarid Loess Plateau

Insight into the response of deep soil moisture (SM) to land cover change is crucial for the sustainable management of regional water resources and ecosystems. However, the deep soil water budget and resilience after revegetation are not clear. In this study, seven typical land-cover patterns (artificial arbor, shrub, economic tree, pasture, abandon grassland, and natural grassland) were selected in four different study sites on the Loess Plateau of China. From May 2019 to December 2020, SM was monitored daily at ten depths along a 10 m profile in situ to assess the effect of revegetation on the vertical distribution, availability, and sustainability of soil water. The results indicated that the minimum SM moved to the deeper depth in the planted forest, while the maximum SM gradually moved to a shallower and deeper depth after the conversion of cropland to forestland and grassland, respectively. The difference in soil water use strategies under different revegetation types generated the opposite effect on deep SM availability, resilience, and sustainability. Conversion of cropland to grassland realized the deep soil available water (SAW) increment by 53.3% over the entire measurement period. Besides, the grassland promoting the deep soil water resilience (SWR) and soil water storage variation (ΔSWS) enhanced by 41.6% and 708.7% below 2 m depth, respectively. Whereas, compared to cropland, afforestation decreased the SAW, SWR, and ΔSWS by 17.2%, 10.5%, and 570.7% below 2 m depth, respectively. Furthermore, the deep SWR and ΔSWS were basically negative in the forestland, resulting in an unbalanced deep soil water budget within the year. Despite the significant disadvantages of sustaining the availability and water budget of deep soil water in artificial vegetation, constructing a reasonable land cover and vegetation structure could achieve the soil–water environment balance and the sustainable utilization of ecosystem water resources in the semiarid Chinese Loess Plateau (CLP). The findings of this study provide valuable references and guidance for sustainable vegetation conservation and water resource management and provide design and optimization solutions for vegetation restoration in similar areas of arid and semiarid regions around the world.

Altitudinal Variation and Soil Characterstics Effect on Forest Communities of North Western Himalayan Region of Uttarakhand, India

The present study was conducted at Dandachali forest of Tehri Forest Division, North-Western part of Himalaya. Surveys and sampling of the vegetation were done using standard ecological assessment methods with an aim to study the soil status at community level. During the study six different forest communities’ viz., Pinus roxburghii- Quercus leucotrichophora mixed forest, Pinus roxburghii pure forest, Pinus roxburghii- Rhododendron arboreum mixed forest, Cedrus deodara- Pinus wallichiana mixed forest, Cedrus deodara- Rhododendron arboreum mixed forest and Rhododendron arboreum- Quercus leucotrichophora mixed forest were taken for the above mentioned study. Importance Value Index (IVI) were observed maximum (271.57) in Pinus roxburghii community then Pinus roxburghii- Quercus leucotrichophora mixed forest IVI (166.69). The soil pH recorded highest (pH 6.60) in Cedrus deodara - Pinus wallichiana mixed forest. Cedrus deodara - Rhododendron arboreum mixed forest community reflected maximum (27.44%) soil moisture content. However organic carbon (1.39%), nitrogen (1202.49 kg/ha.) and phosphorus (31.36 kg/ha) was highest in Pinus roxburghii – Quercus leucotrichophora mixed forest. Potassium observed maximum (351.00 kg/ha.) in Cedrus deodara - Rhododendron arboreum mixed forest. Community wise there was change in soil nutrient status whereas, altitude showed no such variations.

Eco-friendly cellulose-based hydrogels derived from wastepapers as a controlled-release fertilizer

In this study, an eco-friendly controlled release fertilizer cellulose-based hydrogel was prepared from cellulose fibers derived from wastepaper, epichlorohydrin (ECH) as a crosslinker and carboxy methyl cellulose (CMC) as a gelling agent. A maximum swelling capacity of 2000% was achieved for cellulose hydrogel with optimum composition. The soil moisture contents in the presence of optimized cellulose hydrogels were determined using the digital moisture meter. Maximum soil moisture of 36.5% was obtained in topsoil, followed by 30.1% in wet clayey soil and 23.4% in sandy soil after 7 days. Urea as a model fertilizer was loaded onto the cellulose hydrogels to control the release of fertilizer. The maximum loading capacity of urea in cellulose hydrogel is 0.51 g/g. The urea-controlled release profiles of the cellulose hydrogel in distilled water and various types of soils were investigated. The formulation of cellulose hydrogels was observed to facilitate the gradual release of urea, with about 74.71% release in topsoil, 73.37% release in wet clayey soil and 71.84% release in sandy soil within 42 days when compared to the free urea which was about 97.32%, 95.09% and 98.47% release in topsoil, wet clayey soils and sandy soils, respectively within 7 days. The result of this study shows that the urea-loaded cellulose hydrogel could be a promising controlled-release fertilizer.Graphical

Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil

ABSTRACT Climatic drivers of floods have been widely used to improve nonstationary flood frequency analysis (FFA). However, the forecast ability of nonstationary FFA with out-of-sample prediction has not been comprehensively evaluated. We use 379 flood records from Brazil to assess the ability of process-informed nonstationary models for out-of-sample FFA using the generalized extreme value (GEV) distribution. Five drivers of floods are used as covariates: annual temperature, El Nino Southern Oscillation, annual rainfall, annual maximum rainfall, and annual maximum soil moisture content. Our results reveal that a nonstationary model is preferable when there is a significant correlation between flood and climate covariates in both the training period and full record. The rainfall-based covariates lead to better out-of-sample nonstationary FFA models. These findings highlight that using climate information as covariates in nonstationary FFA is a promising approach for estimating future floods and, hence, better infrastructure design, risk assessment and disaster preparedness.

Effect of moisture conservation techniques on almond (Prunus dulcis) productivity under rainfed conditions

A field experiment was conducted to study the effect of moisture conservation techniques on productivity enhancement in almond (Prunus dulcis Mill) during 2010-11 to 2012-13 at Srinagar, Jammu and Kashmir. Treatment sincluded five water harvesting techniques, viz. half-moon (W1), full-moon (W2), cup and plate (W3), trench (W4) and control (W5) and three mulch materials, viz. plastic mulch (M1), organic mulch (M2) and control (M3) were laid out in factorial randomized block design with three replication. The experimental results revealed that maximum mean trunk cross sectional area (266.82 cm2), nut yield (5.37 kg/tree), productivity efficiency (19.47 g/cm2 TCSA) and soil moisture content (13.48%) were recorded in full moon water harvesting technique. Among mulch material, plastic mulch found better in respect to TCSA (255.59 cm2), nut yield (4.99 kg/tree), productivity efficiency (18.88 g/cm2 TCSA) and soil moisture content (12.86%) compared to other treatments. The combined effect of water harvesting and mulching indicated that maximum TCSA (270.48 cm2), nut yield (5.82 kg/tree), productivity efficiency (20.92 g/cm2 TCSA) and soil moisture (13.98 %) were recorded in full moon water harvesting + plastic mulch in almond cultivar Non Pareil under rainfed conditions of north west Himalayan regions of India.

Development and Evaluation of a Deep Learning Based System to Predict District-Level Maize Yields in Tanzania

Prediction of crop yields is very helpful in ensuring food security, planning harvest management (storage, transport, and labor), and performing market planning. However, in Tanzania, where a majority of the population depends on crop farming as a primary economic activity, the digital tools for predicting crop yields are not yet available, especially at the grass-roots level. In this study, we developed and evaluated Maize Yield Prediction System (MYPS) that uses a short message service (SMS) and the Web to allow rural farmers (via SMS on mobile phones) and government officials (via Web browsers) to predict district-level end-of-season maize yields in Tanzania. The system uses LSTM (Long Short-Term Memory) deep learning models to forecast district-level season-end maize yields from remote sensing data (NDVI on the Terra MODIS satellite) and climate data [maximum temperature, minimum temperature, soil moisture, and precipitation (rainfall)]. The key findings reveal that our unimodal and bimodal deep learning models are very effective in predicting crop yields, achieving mean absolute percentage error (MAPE) scores of 3.656% and 6.648%, respectively, on test (unseen) data. This system will help rural farmers and the government in Tanzania make critical decisions to prevent hunger and plan better harvesting and marketing of crops.

Multi-Temporal Assessment of Remotely Sensed Autumn Grass Senescence across Climatic and Topographic Gradients

Climate and topography are influential variables in the autumn senescence of grassland ecosystems. For instance, extreme weather can lead to earlier or later senescence than normal, while higher altitudes often favor early grass senescence. However, to date, there is no comprehensive understanding of key remote-sensing-derived environmental variables that influence the occurrence of autumn grassland senescence, particularly in tropical and subtropical regions. Meanwhile, knowledge of the relationship between autumn grass senescence and environmental variables is required to aid the formulation of optimal rangeland management practices. Therefore, this study aimed to examine the spatial autocorrelations between remotely sensed autumn grass senescence vis-a-vis climatic and topographic variables in the subtropical grasslands. Sentinel 2′s Normalized Difference NIR/Rededge Normalized Difference Red-Edge (NDRE) and the Chlorophyll Red-Edge (Chlred-edge) indices were used as best proxies to explain the occurrence of autumn grassland senescence, while monthly (i.e., March to June) estimates of the remotely sensed autumn grass senescence were examined against their corresponding climatic and topographic factors using the Partial Least Square Regression (PLSR), the Multiple Linear Regression (MLR), the Classification and Regression Trees (CART), and the Random Forest Regression (RFR) models. The RFR model displayed a superior performance on both proxies (i.e., RMSEs of 0.017, 0.012, 0.056, and 0.013, as well as R2s of 0.69, 0.71, 0.56, and 0.71 for the NDRE, with RMSEs and R2s 0.023, 0.018, 0.014 and 0.056, as well as 0.59, 0.60, 0.69, and 0.72 for the Chlred-edge in March, April, May, and June, respectively). Next, the mean monthly values of the remotely sensed autumn grass senescence were separately tested for significance against the average monthly climatic (i.e., minimum (Tmin) and maximum (Tmax) air temperatures, rainfall, soil moisture, and solar radiation) and topographic (i.e., slope, aspect, and elevation) factors to define the environmental drivers of autumn grassland senescence. Overall, the results indicated that Tmax (p = 0.000 and 0.005 for the NDRE and the Chlred-edge, respectively), Tmin (p = 0.021 and 0.041 for the NDRE and the Chlred-edge, respectively), and the soil moisture (p = 0.031 and 0.040 for the NDRE and the Chlred-edge, respectively) were the most influential autumn grass senescence drivers. Overall, these results have shown the role of remote sensing techniques in assessing autumn grassland senescence along climatic and topographic gradients as well as in determining key environmental drivers of this senescence in the study area

Intraseasonal‐to‐seasonal evolution of soil moisture‐based droughts and floods in observation‐based datasets and Coupled Model Intercomparison Project Phase 6 models

AbstractDroughts and floods are extreme events that have tremendous socioeconomic impact. Many studies have investigated their possible variations under the effects of climate changes, while their intraseasonal‐to‐seasonal characteristics have received less attention. Based on observation‐constrained monthly soil moisture (SM) datasets since 1980, we analysed the spatiotemporal characteristics of global SM at the intraseasonal‐to‐seasonal time scales and the associated influencing factors. The results were then used to evaluate the performance of simulations by Coupled Model Intercomparison Project Phase 6 (CMIP6) models. It is found that the frequencies of extreme wet events are slightly higher than those of extreme dry events, but their spatial distributions are similar and are both affected by precipitation variability. Extreme events tend to have longer durations than nonextreme events, and their global mean durations are 4–5 months. Globally, dry events persist longer than wet events. Maximum SM anomalies are strongly affected by earlier precipitation and this effect is stronger in a transitional climate. The onset and peak times of SM anomalies in a year occur most frequently near the peak time of precipitation rather than that of SM. Approximately half of CMIP6 models overestimate the durations of both dry and wet events and approximately one third of models only underestimate wet events durations. Different versions of the same model produce similar event durations. The findings of this study enhance understanding of the intraseasonal‐to‐seasonal variabilities of global droughts and floods and could provide guidance for future study of land–atmosphere interactions and prediction of extreme events.