The Effect of Tied Ridge Cultivation on the Yield of Maize and a Maize/Cowpea Relay in the Gambia

SummaryThe growing season in the semi-arid region of West Africa is drought prone and of irregular duration. Two experiments were conducted in 1986 and 1987 to test the effects of flat cultivation and tied ridge cultivation (TRC) on the yields of maize and the component crops of a maize and cowpea relay cropping system. The two research sites, with slopes of 0.05% and 3%, were near Sapu, The Gambia, on an Aridic Kandiustalf in the 700 mm rainfall zone.Both growing seasons had above average rainfall. In both years, maize on level sites showed no response to tillage practices. On the sloped site in 1987, soil moisture 10 and 15 days after the last rain was greater with TRC than with flat cultivation and yields of sole cowpea and maize were 25% and 18% greater, respectively. On the level site, TRC had no effect on residual soil moisture or grain yield. When rainfall was well distributed, tied ridging did not appear to be a viable tillage alternative for maize-based systems on flat land in central Gambia but with modest slopes, tied ridges markedly increased soil water reserves in the 0.15 to 0.60 m depth after maize harvest.

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Soil water and nitrogen interaction effect on residual soil nitrate and crop nitrogen recovery under maize–wheat cropping system in the semi-arid region of northern India

Agriculture Ecosystems & Environment ◽

10.1016/j.agee.2013.08.001 ◽

2013 ◽

Vol 179 ◽

pp. 108-115 ◽

Cited By ~ 27

Author(s):

Sangeeta Lenka ◽

Anil Kumar Singh ◽

Narendra Kumar Lenka

Keyword(s):

Soil Water ◽

Interaction Effect ◽

Arid Region ◽

Cropping System ◽

Residual Soil ◽

Soil Nitrate ◽

Northern India ◽

Semi Arid Region ◽

Water And Nitrogen Interaction ◽

Semi Arid

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Previous and Current Crop Effects on Early-Season Root Growth and Growing Season’s Soil Moisture Under Dryland Agriculture in Temperate Climate

Journal of Agricultural Science ◽

10.5539/jas.v13n5p50 ◽

2021 ◽

Vol 13 (5) ◽

pp. 50

Author(s):

Kabal S. Gill ◽

Surinder K. Jalota

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Root Growth ◽

Soil Moisture Content ◽

Growing Season ◽

Temperate Climate ◽

Residual Soil ◽

Dryland Agriculture ◽

Early Season ◽

Growing Seasons

Understanding the root growth and changes in soil moisture content during the growing season for dryland agriculture crops can improve crop production. It was hypothesized that early-season root growth might be influenced by previous crop and current crops, and soil moisture content and depletion pattern during the growing season and residual soil moisture may be affected by the crop type. A study was conducted on the early-season root growth of canola (Brassica napus L.), wheat (Triticum aestivum L.), and flax (Linum usitatissimum L.) in 2015; and changes in soil water content during the 2013, 2014, and 2015 growing seasons under canola, flax, wheat, barley (Hordeum vulgare L.), and pea (Pisum sativum L.). Early-season root growth of the canola and flax crops was better on wheat than canola stubble, while for wheat it was similar on the stubbles of both wheat and canola. Soil moisture depletion started relatively earlier under the barley and wheat and later under the flax compared to the canola and pea crops. Flax continued to deplete soil moisture for a longer period than the other crops. With some exceptions, all crops could deplete soil moisture to a similar level (down to about 15% or somewhat lower) by the end of their growing seasons. Generally, almost equal amounts of residual soil moisture remained after the different crops.

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Constraint of soil moisture on CO<sub>2</sub> efflux from tundra lichen, moss, and tussock in Council, Alaska, using a hierarchical Bayesian model

Biogeosciences ◽

10.5194/bg-11-5567-2014 ◽

2014 ◽

Vol 11 (19) ◽

pp. 5567-5579 ◽

Cited By ~ 9

Author(s):

Y. Kim ◽

K. Nishina ◽

N. Chae ◽

S. J. Park ◽

Y. J. Yoon ◽

...

Keyword(s):

Soil Moisture ◽

Environmental Factors ◽

Soil Temperature ◽

Co2 Emission ◽

Emission Rate ◽

Growing Season ◽

The Arctic ◽

Co2 Efflux ◽

Growing Seasons ◽

Tundra Ecosystem

Abstract. The tundra ecosystem is quite vulnerable to drastic climate change in the Arctic, and the quantification of carbon dynamics is of significant importance regarding thawing permafrost, changes to the snow-covered period and snow and shrub community extent, and the decline of sea ice in the Arctic. Here, CO2 efflux measurements using a manual chamber system within a 40 m × 40 m (5 m interval; 81 total points) plot were conducted within dominant tundra vegetation on the Seward Peninsula of Alaska, during the growing seasons of 2011 and 2012, for the assessment of driving parameters of CO2 efflux. We applied a hierarchical Bayesian (HB) model – a function of soil temperature, soil moisture, vegetation type, and thaw depth – to quantify the effects of environmental factors on CO2 efflux and to estimate growing season CO2 emissions. Our results showed that average CO2 efflux in 2011 was 1.4 times higher than in 2012, resulting from the distinct difference in soil moisture between the 2 years. Tussock-dominated CO2 efflux is 1.4 to 2.3 times higher than those measured in lichen and moss communities, revealing tussock as a significant CO2 source in the Arctic, with a wide area distribution on the circumpolar scale. CO2 efflux followed soil temperature nearly exponentially from both the observed data and the posterior medians of the HB model. This reveals that soil temperature regulates the seasonal variation of CO2 efflux and that soil moisture contributes to the interannual variation of CO2 efflux for the two growing seasons in question. Obvious changes in soil moisture during the growing seasons of 2011 and 2012 resulted in an explicit difference between CO2 effluxes – 742 and 539 g CO2 m−2 period−1 for 2011 and 2012, respectively, suggesting the 2012 CO2 emission rate was reduced to 27% (95% credible interval: 17–36%) of the 2011 emission, due to higher soil moisture from severe rain. The estimated growing season CO2 emission rate ranged from 0.86 Mg CO2 in 2012 to 1.20 Mg CO2 in 2011 within a 40 m × 40 m plot, corresponding to 86 and 80% of annual CO2 emission rates within the western Alaska tundra ecosystem, estimated from the temperature dependence of CO2 efflux. Therefore, this HB model can be readily applied to observed CO2 efflux, as it demands only four environmental factors and can also be effective for quantitatively assessing the driving parameters of CO2 efflux.

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Effects of plant density and tillage on growth and grain yield of sorghum (Sorghum bicolor (L.) Moench) under dryland conditions in a semi-arid area in Ethiopia

The Journal of Agricultural Science ◽

10.1017/s0021859600079429 ◽

1987 ◽

Vol 108 (2) ◽

pp. 395-401 ◽

Cited By ~ 2

Author(s):

D. C. Adjei-Twum

Keyword(s):

Soil Moisture ◽

Grain Yield ◽

Plant Density ◽

Grain Filling ◽

Arid Area ◽

Tillage Practices ◽

Available Soil Moisture ◽

Cropping Seasons ◽

Sorghum Bicolor L ◽

Semi Arid

SummaryEffects of plant density ranging from 44444 to 133333 plants/ha and tillage practices (planting in flat beds (control), in the furrows of open ridges, on the top of open ridges, in the furrows of tie-ridges and on the top of tie-ridges) on growth and grain yield of sorghum were investigated at Kobo, a typical semi-arid area in Ethiopia, during 1980, 1981 and 1982 cropping seasons. Plant growth was limited in the flat beds because they were likely to be deficient in soil moisture and sometimes in the tie-ridging treatments, due to waterlogging. However, planting on the top of tie-ridges produced 1·6, 0·4 and 1·8 t/ha more yield than in the flat beds, the method commonly practised by the Kobo farmers, during 1980, 1981 and 1982 respectively. In all seasons, the effect of plant density did not show marked differences. The plants rather adjusted their reproductive growth and development to the seasonal rainfall and presumably to the available soil moisture at the grain-filling periods. It was concluded that the highest plant density did not reach the optimum for the area. Planting sorghum on the top of tie-ridges is recommended.

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Soil moisture regime effect on the performance of watermelon under varying nitrogen levels in a semi-arid region

Journal of Applied Horticulture ◽

10.37855/jah.2004.v06i02.16 ◽

2004 ◽

Vol 06 (02) ◽

pp. 72-75

Author(s):

M.A. Hussaini ◽

A.A. Ramalan ◽

M.K. Othman

Keyword(s):

Soil Moisture ◽

Arid Region ◽

Moisture Regime ◽

Nitrogen Levels ◽

Soil Moisture Regime ◽

Semi Arid Region ◽

Semi Arid

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Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽

10.3390/w12113223 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3223

Author(s):

Hamed Adab ◽

Renato Morbidelli ◽

Carla Saltalippi ◽

Mahmoud Moradian ◽

Gholam Abbas Fallah Ghalhari

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Surface Soil Moisture ◽

Machine Learning Methods ◽

Land Use Types ◽

Semi Arid Region ◽

Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

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Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

Remote Sensing ◽

10.3390/rs10121953 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1953 ◽

Cited By ~ 31

Author(s):

Safa Bousbih ◽

Mehrez Zribi ◽

Mohammad El Hajj ◽

Nicolas Baghdadi ◽

Zohra Lili-Chabaane ◽

...

Keyword(s):

Soil Moisture ◽

Arid Region ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Cloud Model ◽

Remotely Sensed Data ◽

Semi Arid Region ◽

Semi Arid ◽

Sentinel 2 ◽

Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

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