Crop and Location Specific Agricultural Drought Quantification: Part II. Case Study

2017 ◽  
Vol 60 (3) ◽  
pp. 729-739 ◽  
Author(s):  
Rachel L. McDaniel ◽  
Clyde Munster ◽  
J. Tom Cothren
Keyword(s):  
Soil Moisture ◽  
Biomass Production ◽  
Drought Index ◽  
Swat Model ◽  
Growing Season ◽  
Moisture Stress ◽  
Agricultural Drought ◽  
Row Crops ◽  
Moisture Conditions

Abstract. An estimated 70% to 80% of water resources are used for agricultural production. Irrigation helps maintain adequate soil moisture for crops; however, drought can impact both the amount of water required for production and crop yields. Different crops are affected by moisture conditions in different ways, as some can handle lower moisture conditions better than others. There are many drought indices that quantify low-moisture conditions, but they are not crop-specific and therefore do not quantify moisture stress for a given crop. The goal of this study was to evaluate a crop-specific drought index by determining the index’s ability to reflect yield trends due to moisture conditions. The drought index is a weekly index that uses five variables: precipitation, temperature, biomass production, soil moisture, and transpiration. This article presents a case study that examines the effectiveness of the crop-specific drought index in determining moisture stress to crops by comparing the drought index with annual yield values. The site chosen for this study was the upper Colorado River basin (UCRB) in west Texas because it is prone to drought. Cotton is one of the most widely grown row crops in this region and was therefore used in this study. A hydrologic and crop model, the Soil Water Assessment Tool (SWAT), was used to determine the biomass production, soil moisture, and transpiration. Observed precipitation and temperature data were also used both in the SWAT model and in the drought index. A multiple linear regression model was created for each week of the growing season because each variable is important during different weeks of the growing season. For example, in the UCRB, soil moisture was found to be more important during the beginning of the growing season, while biomass production was found to be more important during the end of the growing season. Ultimately, the drought index was found to be a good indicator of moisture-related yield conditions, with an R2 of 0.67. This index can be used to assess moisture stress to agricultural crops and aid in management decisions related to irrigation timing. Keywords: Crop modeling, Drought, Drought index, Hydrologic modeling, SWAT, Water conservation, Water management, Water stress.

Crop and Location Specific Agricultural Drought Quantification: Part III. Forecasting Water Stress and Yield Trends

2017 ◽  
Vol 60 (3) ◽  
pp. 741-752 ◽  
Author(s):  
Rachel L. McDaniel ◽  
Clyde Munster ◽  
John Nielsen-Gammon
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Water Stress ◽  
Water Conservation ◽  
Drought Index ◽  
Swat Model ◽  
Agricultural Drought ◽  
Weather Data ◽  
Drought Prediction ◽  
Yield Trends

Abstract. Agriculture is the largest water consumer, with 70% of global water withdrawals being used for irrigation. Water scarcity issues are being exacerbated by drought and population increases, making efficient water resource management in agricultural production increasingly important. The objective of this article is to evaluate the use of short-term weather forecasts for agricultural drought prediction. A crop-specific, linear regression drought analysis technique was used in this study. This study takes place in the upper Colorado River basin (UCRB) in west Texas. Five variables associated with agricultural drought (precipitation, temperature, biomass production, soil moisture depletion, and transpiration) were scaled and used to estimate cotton yields. The yield percentiles were used as a drought index. Precipitation and temperature were forecasted with a two-week lead time using probable scenarios based on historical data. The other three variables were estimated using the SWAT model. Forecasts were generated for each week of the growing season from 2010 through 2013. Four statistics were used to evaluate model performance, including the Nash-Sutcliffe coefficient of efficiency (NSE), the coefficient of determination (R2), and two error indices, the percent bias (PBIAS) and the RMSE-observations standard deviation ratio (RSR). Comparing the variables using the forecasted weather data to those using the observed weather data revealed that four of the five performed satisfactorily. Temperature performed the best statistically, with an NSE of 0.85 and PBIAS of 9.4%. Precipitation (NSE = 0.51, PBIAS = -34%), cumulative biomass (NSE = 0.69, PBIAS = -38%), and transpiration (NSE = 0.53, PBIAS = 11%) also performed well. However, the soil moisture depletion forecasts (NSE = 0.28, PBIAS = 11%) were unsatisfactory. The forecasted cotton yield trends (NSE = 0.72, PBIAS = -12%) and drought index (NSE = 0.76, PBIAS = -13%) both performed satisfactorily, indicating that this forecasting method may be used for decision making related to agricultural water management, including irrigation timing. Keywords: Crop modeling, Drought, Drought index, Forecasting, Hydrologic modeling, SWAT, Water conservation, Water management, Water stress.

Crop and Location Specific Agricultural Drought Quantification: Part I. Method Development

2017 ◽  
Vol 60 (3) ◽  
pp. 721-728 ◽  
Author(s):  
Rachel L. McDaniel ◽  
Clyde Munster ◽  
J. Tom Cothren
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Water Stress ◽  
Biomass Production ◽  
Water Conservation ◽  
Drought Index ◽  
Method Development ◽  
Normal Values ◽  
Agricultural Drought ◽  
Specific Information

Abstract. The prevailing definition of drought is low moisture conditions over a period of time; however, no single definition exists for drought. The numerous drought definitions and classifications have led to many indices that attempt to quantify drought. Most of these indices rely on a single variable, such as precipitation or soil moisture, and do not consider crop-specific information such as threshold values, which cause crop stress when exceeded. An example of a crop threshold is the soil moisture value below which the crop experiences stress. The goal of this study was to provide a new methodology to quantify drought for a specific crop at a specific location, allowing water management decisions on a crop-specific basis. This was achieved by scaling and combining five factors: precipitation, temperature, biomass production, soil moisture, and transpiration. The scaled temperature and soil moisture are calculated using crop-specific stress thresholds, whereas the scaled precipitation is calculated by using location-specific normal values. Transpiration stress is a crop and location specific value that is calculated by comparing the actual transpiration to the daily maximum transpiration. The biomass production is also a crop and location specific value that uses the normal values for linear scaling. The variables are combined with multiple linear regression models that estimate crop yields. A single model is created for each week of the growing season using the variable or variables that are significant for that week. The predicted yield deciles indicate the yield trend based on crop water stress and are therefore used as the crop-specific drought index. Keywords: Crop modeling, Drought, Drought index, Hydrologic modeling, SWAT, Water conservation, Water management, Water stress.

IRRIGATION MANAGEMENT OF ALFALFA FOR SEED

1977 ◽  
Vol 57 (3) ◽  
pp. 891-896 ◽  
Author(s):  
K. K. KROGMAN ◽  
E. H. HOBBS
Keyword(s):  
Soil Moisture ◽  
Medicago Sativa ◽  
Seed Production ◽  
Irrigation Management ◽  
Growing Season ◽  
Moisture Stress ◽  
Field Plot ◽  
Southern Alberta ◽  
Medicago Sativa L ◽  
Seed Yields

In field plot experiments conducted in southern Alberta over a 6-yr period, highest seed yields of alfalfa (Medicago sativa L. cv. Beaver) were obtained with one or two irrigations in the first half of the growing season. In outdoor lysimeters protected from rain, moisture stress for more than 8 days before seed ripening severely reduced seed production. Under field conditions, stored soil moisture from irrigation during the vegetative stage of growth plus occasional rain in July and August permitted irrigation of alfalfa for seed to be stopped at the bud to early bloom stage (June to early July).

Effects of soil moisture and temperature on seedling emergence from natural and pre-germinated onion seeds

1986 ◽  
Vol 107 (2) ◽  
pp. 249-256 ◽  
Author(s):  
W. E. Finch-Savage
Keyword(s):  
Soil Moisture ◽  
Water Availability ◽  
Seedling Emergence ◽  
Moisture Stress ◽  
Start Time ◽  
Mean Temperature ◽  
Changing Patterns ◽  
Moisture Conditions ◽  
Germinating Seeds ◽  
Germinated Seeds

SummaryThe emergence of seedlings from natural, germinating and selected uniformlygerminated onion seeds was compared in a range of changing patterns of soil moisture. The timing, spread and amount of seedling emergence from seeds in all three treatments were affected by the timing of water availability in the seed bed and these effects differed between treatments.The rate of seedling emergence in all three treatments under non-limiting soil moisture conditions was correlated with mean temperature, but this relationship was obscured in irrigation treatments where water stress occurred. However, if the seed bed was moist at sowing irrespective of subsequent moisture stress the reciprocals of the time to the start, time to 50% and time to the end of seedling emergence from uniformly germinated seeds were correlated with mean temperature (r > 0·87, D.F. 27).The results show that if the seed bed is irrigated prior to sowing and soil moisture is maintained during the first 3 days following sowing high levels of seedling emergence with both predictable timing and uniformity can be achieved by sowing uniformlygerminated seeds. Seedling emergence from natural and germinating seeds was much less predictable.

scholarly journals Satellite Soil Moisture Validation Using Hydrological SWAT Model: A Case Study of Puerto Rico, USA

Hydrology ◽  
2017 ◽  
Vol 4 (4) ◽  
pp. 45 ◽  
Author(s):  
Aditya Nilawar ◽  
Cassandra Calderella ◽  
Tarendra Lakhankar ◽  
Milind Waikar ◽  
Jonathan Munoz
Keyword(s):  
Puerto Rico ◽  
Soil Moisture ◽  
Swat Model

scholarly journals Effect of Irrigation and Soil Water Stress on Densities of Macrophomina phaseolina in Soil and Roots of Two Soybean Cultivars

Plant Disease ◽  
2000 ◽  
Vol 84 (8) ◽  
pp. 895-900 ◽  
Author(s):  
S. R. Kendig ◽  
J. C. Rupe ◽  
H. D. Scott
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Soil Water ◽  
Growth Stage ◽  
Root Colonization ◽  
Growing Season ◽  
Moisture Stress ◽  
Macrophomina Phaseolina ◽  
Soil Moisture Stress ◽  
Soil Water Stress

The effects of irrigation and soil water stress on Macrophomina phaseolina microsclerotial (MS) densities in the soil and roots of soybean were studied in 1988, 1989, and 1990. Soybean cvs. Davis and Lloyd received irrigation until flowering (TAR2), after flowering (IAR2), full season (FSI), or not at all (NI). Soil water matric potentials at 15- and 30-cm depths were recorded throughout the growing season and used to schedule irrigation. Soil MS densities were determined at the beginning of each season. Root MS densities were determined periodically throughout the growing season. Microsclerotia were present in the roots of irrigated as well as nonirrigated soybean within 6 weeks after planting. By vegetative growth stage V13, these densities reached relatively stable levels in the NI and FSI treatments (2.23 to 2.35 and 1.35 to 1.63 log [microsclerotia per gram of dry root], respectively) through reproductive growth stage R6. After R6, irrigation was discontinued and root densities of microsclerotia increased in all treatments. Initiation (IAR2) or termination (TAR2) of irrigation at R2 resulted in significant changes in root MS densities, with densities reaching levels intermediate between those of FSI and NI treatments. Year to year differences in root colonization reflected differences in soil moisture due to rainfall. The rate of root colonization in response to soil moisture stress decreased with plant age. Root colonization was significantly greater in Davis than Lloyd at R5 and R8. This was reflected in a trend toward higher soil densities of M. phaseolina at planting in plots planted with Davis than in plots planted with Lloyd. Although no charcoal rot symptoms in the plant were observed in this study, these results indicated that water management can limit, but not prevent, colonization of soybean by M. phaseolina, that cultivars differ in colonization, and that these differences may affect soil densities of the fungus.

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