Corn Yield and Nutrient Uptake as Affected by Water‐Table Depth and Soil Submergence 1

1975 ◽  
Vol 67 (6) ◽  
pp. 745-749 ◽  
Author(s):  
T. N. Chaudhary ◽  
V. K. Bhatnagar ◽  
S. S. Prihar
2014 ◽  
Vol 11 (3) ◽  
pp. 577-599 ◽  
Author(s):  
M. Mezbahuddin ◽  
R. F. Grant ◽  
T. Hirano

Abstract. Seasonal variation in water table depth (WTD) determines the balance between aggradation and degradation of tropical peatlands. Longer dry seasons together with human interventions (e.g. drainage) can cause WTD drawdowns making tropical peatland C storage highly vulnerable. Better predictive capacity for effects of WTD on net CO2 exchange is thus essential to guide conservation of tropical peat deposits. Mathematical modelling of basic eco-hydrological processes under site-specific conditions can provide such predictive capacity. We hereby deploy a process-based mathematical model ecosys to study effects of seasonal variation in WTD on net ecosystem productivity (NEP) of a drainage affected tropical peat swamp forest at Palangkaraya, Indonesia. Simulated NEP suggested that the peatland was a C source (NEP ~ −2 g C m−2 d−1, where a negative sign represents a C source and a positive sign a C sink) during rainy seasons with shallow WTD, C neutral or a small sink (NEP ~ +1 g C m−2 d−1) during early dry seasons with intermediate WTD and a substantial C source (NEP ~ −4 g C m−2 d−1) during late dry seasons with deep WTD from 2002 to 2005. These values were corroborated by regressions (P < 0.0001) of hourly modelled vs. eddy covariance (EC) net ecosystem CO2 fluxes which yielded R2 > 0.8, intercepts approaching 0 and slopes approaching 1. We also simulated a gradual increase in annual NEP from 2002 (−609 g C m−2) to 2005 (−373 g C m−2) with decreasing WTD which was attributed to declines in duration and intensity of dry seasons following the El Niño event of 2002. This increase in modelled NEP was corroborated by EC-gap filled annual NEP estimates. Our modelling hypotheses suggested that (1) poor aeration in wet soils during shallow WTD caused slow nutrient (predominantly phosphorus) mineralization and consequent slow plant nutrient uptake that suppressed gross primary productivity (GPP) and hence NEP (2) better soil aeration during intermediate WTD enhanced nutrient mineralization and hence plant nutrient uptake, GPP and NEP and (3) deep WTD suppressed NEP through a combination of reduced GPP due to plant water stress and increased ecosystem respiration (Re) from enhanced deeper peat aeration. These WTD effects on NEP were modelled from basic eco-hydrological processes including microbial and root oxidation-reduction reactions driven by soil and root O2 transport and uptake which in turn drove soil and plant carbon, nitrogen and phosphorus transformations within a soil-plant-atmosphere water transfer scheme driven by water potential gradients. Including these processes in ecosystem models should therefore provide an improved predictive capacity for WTD management programs intended to reduce tropical peat degradation.


2017 ◽  
Vol 14 (2) ◽  
pp. 46-55 ◽  
Author(s):  
Binny Dasila ◽  
Veer Singh ◽  
HS Kushwaha ◽  
Ajaya Srivastava ◽  
Shri Ram

Lysimeter experiment was conducted at Govind Ballabh Pant University of Agriculture & Technology, Pantnagar during summer season 2013 to study the effect of irrigation schedules and methods on yield, nutrient uptake and water use efficiency of cowpea as well as nutrient loss from silty clay loam soil under fluctuating water table conditions. The experiment was laid out in factorial randomized block design having three irrigation schedules at IW/CPE ratio of 0.3. 0.2 and 0.15 with two irrigation methods (flood and sprinkler) and at 30±1.5, 60±1.5 and 90±1.5 cm water tables replicated thrice. Maximum root length (129.4 cm) and root length density (0.395 cm/cm3) were obtained when irrigation was scheduled at IW: CPE 0.3 associated with 30±1.5 cm water table depth using sprinkler method. Increase in water table depth and IW: CPE ratio decreased water use efficiency where IW: CPE 0.3 produced highest grain yield (1411.6 kg ha-1) with the WUE of 1.15 kg ha mm-1. Significant nutrients uptake response was observed owing to variation in water table depth, irrigation schedules and methods. Analysis of lysimeter leached water showed that with deep drainage and more IW:CPE, leaching losses of N,P and K were more however water applied through sprinkler saved 20.1, 53.7 and 24.4% N, P and K, respectively, over flooded method. Irrigation given at IW: CPE 0.3 through sprinkler form at 60±1.5 cm water table depth favours the higher grain yield and nutrient uptake by crop whereas flooded irrigation with deep water table condition accelerated nutrient leaching.SAARC J. Agri., 14(2): 46-55 (2016)


1980 ◽  
Vol 44 (2) ◽  
pp. 301-305 ◽  
Author(s):  
B. D. Meek ◽  
E. C. Owen-Bartlett ◽  
L. H. Stolzy ◽  
C. K. Labanauskas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2148
Author(s):  
Jonathan A. Lafond ◽  
Silvio J. Gumiere ◽  
Virginie Vanlandeghem ◽  
Jacques Gallichand ◽  
Alain N. Rousseau ◽  
...  

Integrated water management has become a priority for cropping systems where subirrigation is possible. Compared to conventional sprinkler irrigation, the controlling water table can lead to a substantial increase in yield and water use efficiency with less pumping energy requirements. Knowing the spatiotemporal distribution of water table depth (WTD) and soil properties should help perform intelligent, integrated water management. Observation wells were installed in cranberry fields with different water management systems: Bottom, with good drainage and controlled WTD management; Surface, with good drainage and sprinkler irrigation management; Natural, without drainage, or with imperfectly drained and conventional sprinkler irrigation. During the 2017–2020 growing seasons, WTD was monitored on an hourly basis, while precipitation was measured at each site. Multi-frequential periodogram analysis revealed a dominant periodic component of 40 days each year in WTD fluctuations for the Bottom and Surface systems; for the Natural system, periodicity was heterogeneous and ranged from 2 to 6 weeks. Temporal cross correlations with precipitation show that for almost all the sites, there is a 3 to 9 h lag before WTD rises; one exception is a subirrigation site. These results indicate that automatic water table management based on continuously updated knowledge could contribute to integrated water management systems, by using precipitation-based models to predict WTD.


Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.


Oecologia ◽  
2021 ◽  
Author(s):  
Jonathan W. F. Ribeiro ◽  
Natashi A. L. Pilon ◽  
Davi R. Rossatto ◽  
Giselda Durigan ◽  
Rosana M. Kolb

2010 ◽  
Vol 40 (8) ◽  
pp. 1485-1496 ◽  
Author(s):  
Sakari Sarkkola ◽  
Hannu Hökkä ◽  
Harri Koivusalo ◽  
Mika Nieminen ◽  
Erkki Ahti ◽  
...  

Ditch networks in drained peatland forests are maintained regularly to prevent water table rise and subsequent decrease in tree growth. The growing tree stand itself affects the level of water table through evapotranspiration, the magnitude of which is closely related to the living stand volume. In this study, regression analysis was applied to quantify the relationship between the late summer water table depth (DWT) and tree stand volume, mean monthly summertime precipitation (Ps), drainage network condition, and latitude. The analysis was based on several large data sets from southern to northern Finland, including concurrent measurements of stand volume and summer water table depth. The identified model demonstrated a nonlinear effect of stand volume on DWT, a linear effect of Ps on DWT, and an interactive effect of both stand volume and Ps. Latitude and ditch depth showed only marginal influence on DWT. A separate analysis indicated that an increase of 10 m3·ha–1 in stand volume corresponded with a drop of 1 cm in water table level during the growing season. In a subsample of the data, high bulk density peat showed deeper DWT than peat with low bulk density at the same stand volume.


2021 ◽  
Vol 131 ◽  
pp. 108122
Author(s):  
Thomas G. Sim ◽  
Graeme T. Swindles ◽  
Paul J. Morris ◽  
Andy J. Baird ◽  
Dan J. Charman ◽  
...  

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