Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA

2022 ◽  
Vol 259 ◽  
pp. 107234
Author(s):  
Bruno Patias Lena ◽  
Luca Bondesan ◽  
Everton Alves Rodrigues Pinheiro ◽  
Brenda V. Ortiz ◽  
Guilherme Trimer Morata ◽  
...  
Keyword(s):  
Field Capacity ◽  
Irrigation Scheduling ◽  
Hydrus 1D

scholarly journals PENENTUAN WAKTU TANAM PADI SAWAH DI KABUPATEN SANGIHE DENGAN MENGGUNAKAN MODEL SIMULASI SHIERARY RICE

EUGENIA ◽  
2015 ◽  
Vol 21 (3) ◽  
Author(s):  
Farida Fattah ◽  
J. E.X. Rogi ◽  
Mariam M. Toding
Keyword(s):  
Cost Savings ◽  
Simulation Models ◽  
Field Capacity ◽  
Paddy Rice ◽  
Yield Potential ◽  
Potential Yield ◽  
Planting Time ◽  
The North ◽  
Humidity Field

ABSTRACT   Decline in rice production in Sangihe Island partly due to climate change and the implementation of a less precise planting time. Rice Shierary  model was used to predict the timing of planting paddy and has advantages in time and cost savings. This study aims to determine the exact time of planting in paddy rice crops in the district of South Manganitu, North Tabukan and Tamako, as well as to determine whether the results of the simulation model Shierary Rice together with the results obtained from the Central Bureau of Statistic Sangihe Islands. Input model consists of solar radiation, rainfall, temperature and humidity, field capacity, permanent wilting point, varieties, irrigation, nitrogen fertilization, longitude and latitude of the study area. While the output was paddy rice yield potential. It can be concluded that (a) the appropriate planting time of paddy in the South Manganitu namely in June with a potential yield of 4.25 tonha-1 and March with a potential yield of 3.88 tonha-1. (b) the appropriate planting time of paddy in the  North Tabukan namely in June with a potential yield of 4.27 tonha-1 and March with a potential yield of 3.62 tonha-1. (c) the appropriate planting time of paddy in Tamako ie in March with a potential yield of 3.58 tonha-1 and February with a potential yield of 3.28 tonha-1. (d) the yield of rice field paddy with the determination of planting time by using the model of Shierary Rice nearing/line with productivity results which obtained by the Central Statistics Agency (BPS) Sangihe Islands. Keywords: planting, paddy, simulation models, Shierary Rice

scholarly journals Material flow determination through agricultural machinery management

2010 ◽  
Vol 67 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Thiago Libório Romanelli ◽  
Marcos Milan
Keyword(s):  
Fuel Consumption ◽  
Agricultural Production ◽  
Material Flow ◽  
Production Systems ◽  
Field Capacity ◽  
Effective Field ◽  
Silage Production ◽  
Definition Of ◽  
Agricultural Production Systems

The approach of material embodiment in agricultural production systems is important because it determines the convergence of inputs (indirectly, the natural resources) to the field. Additionally, material flow is the basis for both environmental (energy analysis, emergy synthesis, life-cycle analysis and carbon inventories) and economical analyses. Since different materials cannot compose a single index, generally these flows are not shown, making comparisons among approaches difficult. Another aspect that makes comparisons difficult is the definition of the boundary of the studied system. If these boundaries differ, results will also be different, hiding actual distinctions among systems. The present study aims to suggest an arrangement of existing models to determine material flow in agricultural production systems. The following steps were considered: i) the adoption of a diagram language to represent the analyzed system; ii) determination of the material flow for directly applied inputs; iii) determination of the material flow for indirectly applied inputs, which included: determination of the effective field capacity; fuel consumption; machinery depreciation; and labor. Data on fuel consumption were compared with the models presented. The best model applied was a fixed parameter based on engine power (0.163 L kW-1 h-1). The determination of the material flow for maize silage production presented similar results as those obtained in regional databases.

Water requirements of olive orchards–II: determination of crop coefficients for irrigation scheduling

2005 ◽  
Vol 24 (2) ◽  
pp. 77-84 ◽  
Author(s):  
F. Orgaz ◽  
L. Testi ◽  
F.J. Villalobos ◽  
E. Fereres
Keyword(s):  
Irrigation Scheduling ◽  
Water Requirements ◽  
Crop Coefficients

scholarly journals Validation of irrigation scheduling for cultivation of irrigated wheat in semi-arid climatic condition of Ethiopia

2018 ◽  
Vol 6 (2) ◽  
pp. 139-147
Author(s):  
Elias Kebede ◽  
Yonas Derese ◽  
Nigussie Abebe ◽  
Fikadu Robi ◽  
Kebede Nanesa
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Irrigation Water ◽  
Field Trial ◽  
Short Interval ◽  
Field Capacity ◽  
Irrigation Scheduling ◽  
Irrigation Regime ◽  
Irrigation Application ◽  
Irrigated Wheat

ABSTRACTThis study was conducted for three years (2014-2016) to validate irrigation scheduling of irrigated wheat cultivation to determine appropriate irrigation regime.  The experiments were irrigation scheduling based on CROPWAT Model 8.0 and validation on field trial. The treatments were arranged in randomized complete block design with three replications. The field trial was involving three irrigation regime treatments were used for comparison. The treatments were Treatment 1 (T1): Optimal irrigation regime as determined by Cowpat for windows that provides irrigation water of D1=50mm at an interval of I1=7 days, Treatment 2(T2): Optimal irrigation regime as determined by Cowpat for windows that provides irrigation water of D2=67mm at an interval of I2=10 days. Treatment 3(T3): Optimal irrigation regime as determined by Cowpat for windows that provides irrigation water of D3=108.3mm at an interval of I3=15 days. Treatment 4(T4): An irrigation regime that provides irrigation water at critical soil moisture depletion and an amount that would refill the soil moisture depletion to field capacity. Result indicated that grain yield was significantly affected by irrigation levels. Irrigation regime of Treatment 4 produced higher grain yield 2400 kg/ha and 20.0q/ha in 2015 and 2016 cropping season. The highest mean yield of wheat (2200 kg/ha) was obtained from critical moisture refill field capacity irrigation application. Whereas, the lowest mean yield (1778 kg/ha) was obtained from T3, 7 days irrigation interval and 50mm irrigation application. This indicates that yield of wheat decrease with decreasing water amount and short interval frequency. Irrigation scheduling based on cowpat model with irrigation regime that provides irrigation water at critical soil moisture depletion and an amount that would refill the soil moisture depletion to field capacity found promising optimum wheat scheduling under Werer and similar areas.

scholarly journals Revisiting Field Capacity (FC): variation of definition of FC and its estimation from pedotransfer functions

2014 ◽  
Vol 38 (6) ◽  
pp. 1750-1764 ◽  
Author(s):  
Theophilo Benedicto Ottoni Filho ◽  
Marta Vasconcelos Ottoni ◽  
Muriel Batista de Oliveira ◽  
José Ronaldo de Macedo ◽  
Klaus Reichardt
Keyword(s):  
Moisture Content ◽  
Soil Properties ◽  
Field Capacity ◽  
Pedotransfer Functions ◽  
Soil Morphology ◽  
Soil Water Retention ◽  
Input Variables ◽  
Definition Of

Taking into account the nature of the hydrological processes involved in in situ measurement of Field Capacity (FC), this study proposes a variation of the definition of FC aiming not only at minimizing the inadequacies of its determination, but also at maintaining its original, practical meaning. Analysis of FC data for 22 Brazilian soils and additional FC data from the literature, all measured according to the proposed definition, which is based on a 48-h drainage time after infiltration by shallow ponding, indicates a weak dependency on the amount of infiltrated water, antecedent moisture level, soil morphology, and the level of the groundwater table, but a strong dependency on basic soil properties. The dependence on basic soil properties allowed determination of FC of the 22 soil profiles by pedotransfer functions (PTFs) using the input variables usually adopted in prediction of soil water retention. Among the input variables, soil moisture content θ (6 kPa) had the greatest impact. Indeed, a linear PTF based only on it resulted in an FC with a root mean squared residue less than 0.04 m³ m-3 for most soils individually. Such a PTF proved to be a better FC predictor than the traditional method of using moisture content at an arbitrary suction. Our FC data were compatible with an equivalent and broader USA database found in the literature, mainly for medium-texture soil samples. One reason for differences between FCs of the two data sets of fine-textured soils is due to their different drainage times. Thus, a standardized procedure for in situ determination of FC is recommended.

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