HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Search for Related Content PubMed Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Agricola Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Related Collections Other Crop Management Crop Models Other Models Irrigation

A Model to Predict Safe Stages of Development for Rice Field Draining and Field Tests of the Model Predictions in the Arkansas Grand Prairie

^a Univ. of Arkansas, Rice Res. and Ext. Ctr., 2900 Highway 130 East, Stuttgart, AR 72160
^b Dep. of Crop, Soil and Environmental Sci., Univ. of Arkansas, Fayetteville, AR 72701
^c Dep. of Food Sci., Univ. of Arkansas, Fayetteville, AR 72701. Paper published with the approval of the Director, Arkansas Agric. Exp. Stn., Univ. of Arkansas, Fayetteville, AR 72701

^* Corresponding author (pcounce{at}uark.edu).

	ABSTRACT

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
Due to the cost of extracting water, effective and efficient utilization of irrigation water for rice (Oryza sativa L.) is critical to rice farm profitability. The objective of this study is to predict safe stages of development for draining rice. This objective has the potential of saving rice farmers water. A computer program has been developed to predict the stage of development for draining water from rice field soils at which the risk of reduced grain yield or milling quality from insufficient water is considered to be near zero. The parameters of the model are predictions of (i) temperature during rice reproductive growth stages (RRGS) starting at R3, (ii) timing of various RRGS, (iii) maximum amount of water used by the rice crop at each growth stage, and (iv) the water held in the soil profile after draining which is available to the rice crop. The central goals of the model are to allow draining at an RRGS in which (a) the danger of reducing yield and quality from water deficits is at a minimum and (b) water is conserved and land conditions for harvest are improved. Experiments to test the predictions were conducted in 2005 and 2006 at two Arkansas locations: Gillett and Stuttgart. An experiment was also conducted at DeWitt, AR, in 2006. Draining at stages of development predicted by the model did not affect yield milling quality relative to the control for any year or location. Predicted water savings from reduced irrigation ranged between $10.26 to $55.44 ha^–1 depending on pump depth. Implementation of the program can save money, reduce tillage costs, and reduce unnecessary depletion of the aquifers.

Abbreviations: RRGS, rice reproductive growth stage • GDD, growing degree days • HRY, head rice yields

Received for publication October 15, 2007.

	INTRODUCTION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
SEVERAL IMPORTANT REASONS for conserving water in rice production exist. Subsurface water is expensive to extract, the aquifers are being depleted at an unsustainable rate, water is needed for irrigating other crops such as soybean [Glycine max (L.) Merr.] at critical reproductive stages of development, and leaving water on the field too long can result in increased tillage costs, harvesting problems, and crop loss. A typical irrigation of a rice field will be approximately 7.62 cm (or 3 in), which costs from $10.26 to 55.44 ha^–1 (Table 1 ). The cost of extracting water for irrigation is a significant proportion of the costs of producing rice (Watkins et al., 2006). Irrigation from an aquifer is not sustainable when average withdrawal rates from an aquifer exceed average recharge rates (Czarmecki et al., 2003; Custudio, 2002; Sophocleous, 2000). Aquifer depletion is a matter of public record: in eastern Arkansas and withdrawal rates have exceeded recharge over a period of at least 40 yr (Scott et al., 1998). Moreover, at the time of year in which rice irrigation water may be reduced, soybean yields respond most to irrigation and irrigation is often unavailable to soybean (Popp et al., 2005). If rice lodges before draining, seeds will rapidly sprout or deteriorate and yield and quality loss can be extensive. Consequently, growers need to know when rice can be drained without reducing grain yield or milling quality.

Since the draining research described by Counce et al. (1990, 1993) was completed, a rice growth staging system has been developed to allow clear communication among farmers, researchers, extension personnel, and others working with rice (Counce et al., 2000). Research on the growth-staging project has allowed time intervals to be established between the different RRGS after heading (Watson et al., 2005; Clements et al., 2003). This is partially because of the objective features of the staging system, which allows clear determination of each growth stage by comparing plants with criteria which are either present or absent (Counce et al., 2000). Tests indicated rice yield was sensitive to water stress through grain maturity (Yoshida, 1981; P.A. Counce, personal communication, 2008). This is true for a large number of grain and seed crops. For instance, corn (Zea mays L.), soybean, and grain sorghum (Sorghum bicolor L.) crop yield can be reduced due to water deficits until physiological maturity of grain or seeds (Klocke et al., 1991).

Rice can in some cases be drained at 2 wk after 50% heading without reducing yield or quality but the plant is sensitive to drought stress until the kernels have filled. The soil profile holds water available for plant uptake after draining which can prevent drought stress. Furthermore, as the crop approaches maturity the amount of water used by the crop progressively declines.

To aid rice producers in end-of-season draining decisions, a computer program was developed to predict the safe growth stage for draining rice fields–the growth stage at which rice can be safely drained. Input data needed are soil series, rooting depth, and projected (or actual) date of 50% heading. Outputs from the program are a predicted growth stage and date of that stage for safely draining the rice field without reducing grain yield or milling quality.

Acceptance of early draining concepts by farmers has been hindered by the lack of a definitive framework for making the decision. One objective of this research was to first develop a predictive model for safe draining based on soil characteristics, crop developmental rate, and crop water use. The second objective was to evaluate differences in grain yield and milling quality when soil was drained at developmental stages predicted by our model compared to conventional later dates.

	MATERIALS AND METHODS

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
The Model
The model consists of four information components: (i) a historical temperature database for Stuttgart, AR, (ii) a database for growing degree days (GDD) per RRGS, (iii) maximum water use projections per RRGS, and (iv) soil water availability to the rice crop determined from soil type and published water availability values for various soils.

Historical Temperature Database
The weather station at the University of Arkansas Rice Research and Extension Center has been in service for more than 56 yr. The temperatures from the mean of these years were used to project GDD for the crop in 2006. In 2005, the assumed daily GDD projection was 25 (see below Eq. [1]). The projected temperature maximum and minimum values were taken from the historical database based on the 50% heading date (date when 50% of the expected panicles have emerged from a given plot or field). The historical weather database was used in 2006 but in 2005, an assumption was made based on a slightly different database (see next section Growing Degree Days Projections of Reproductive Stages of Development).

Growing Degree Days Projections of Reproductive Stages of Development
The GDD projections for various rice developmental milestones were developed several years ago (Keisling et al., 1984). These projections have been used extensively in the southeastern U.S. rice producing areas. The rice growth staging system was developed to provide an objective, adaptive, and uniform method for describing rice development (Counce et al., 2000). There are 10 RRGS for the rice growth staging system. Reproductive development consists of 10 growth stages based on discrete morphological criteria for the main stem panicle: panicle initiation (R0), differentiation of panicle branches (R1), flag leaf collar formation (R2), panicle exertion (R3), anthesis (R4), grain length expansion to the end of the hull has begun for at least one main stem caryopsis (R5), at least one main stem caryopsis has completely elongated to the end of the hull (R6), at least one main stem grain has turned yellow (R7), at least one main stem grain has turned brown (R8) and complete panicle maturity (R9). By observing daily growth stages, GDD projections between successive RRGS were determined (Table 2 ). From these determinations, the periods of time between successive stages of development can also be calculated:

[1]

where T_A is the average of daily maximum (T_max) and daily minimum (T_min) air temperatures. T_B is the base temperature below which development is presumed to cease (Dwyer et al., 1999). The term t refers to the time step for the calculation, in this case, 1 d. In rice, the base temperature is set at 10°C (Keisling et al., 1984). Furthermore, development above 34.4°C for T_A and above 21.1°C for T_B were expected to result in no increase in development. If GDD is computed as less than zero then it is set to zero.

[2]

where d_rrgs = days per rice reproductive growth stage and GDD_d = days per growing degree days in a selected time period beginning at the R3 stage. Temperature projections were used to generate dates at which each successive growth stage would be reached. These dates were determined from the historical weather database in 2006. In 2005, 25 GDD d^–1 were predicted to be accumulated.

Maximum Water Use by the Rice Crop per Rice Reproductive Growth Stage
Published water use amounts from field experiments form the basis for this part of the model (Lage et al., 2003; Renaud et al., 2000). Lage et al. (2003) presented maximum water use at the R3 growth stage. Maximum water use at other growth stages was estimated from season-long measurements (Lage et al., 2003; Renaud et al., 2000). We multiplied maximum water use per day for each RRGS (Table 3 ) by the projected number of days for the RRGS. The product was the maximum water use per RRGS.

[3]

Where MCWU_rrgs = maximum crop water use per rice reproductive growth stage and MCWU_d = maximum crop water use per day at each respective RRGS (Table 3) and d_rrgs from Eq. [2].

[4]

where ASW is the potentially available soil water (in mm) for uptake by the rice crop, _sat and WR₁₅₀₀ are as defined above, and Dz is the thickness of the rooting zone (in millimeters). The amount of water available to the rice crop at draining (i.e., ASW) for the DeWitt and Stuttgart soils were 0.429 and 0.384 mm mm^–1, respectively.

Depth of the Root Zone
Since >95% of the rice roots have been shown to be concentrated in the top 200 mm of soil (Sharma et al., 1994; Beyrouty et al., 1996), it was assumed that no water is extracted below the 200-mm soil depth even in the absence of a tillage pan. Penetrometer readings were made, when the soil was at or near saturation, to a depth of 400 mm for each site. The rooting depth was assumed to be reached with the penetrometer when the soil resistance reached 2.0 MPa (Gajri et al., 2002). The plow layer in these Prairie soils is so pronounced, however, that it can be clearly determined with a shovel, soil probe, or steel bar. With the exception of the Gillett location in 2005, the depth of the root zone was 100 mm. At Gillett in 2005, the rooting depth was 200 mm due to the 2.0-MPa impedance reading being reached at that depth. The amount of water available to the crop was determined by Eq. [4] using the appropriate rooting depth for each location.

Synthesis
The model that was developed is a specialized, yet conservative and practical, water budget for a rice crop. Beginning with the R9 stage of development and working backward, water use is summed with the amount of water extracted at the R8 stage. The amount of water extracted between the R8 to R9 stage was compared to the amount of water remaining in the soil. If there is more available water in the soil than the crop extracts from the R8 to R9 stage, the amount of water extracted from the R7 to R9 stage is compared to the available soil water. If there is more water in the soil than the crop extracts from the R7 to R9 stage, the amount of water extracted from the R6 to R9 stage is compared to the available soil water. The soil contains a reservoir of available water when draining is complete. Beginning at maturity and working in a stepwise manner backward from maturity, the water extracted is summed. As long as the water extracted from an RRGS to maturity is less than or equal to the amount of water in the soil reservoir, it is assumed safe to drain at that stage. The program allows for the determination of the earliest RRGS at which plant extraction is less than or equal to the amount of water available in the soil.

Experiments
Five field experiments were conducted over 2 yr. Each experiment was arranged as a randomized complete block with four replications. Data were submitted to analysis of variance. There were two treatments in all experiments: (i) drain at the safe growth stage as determined by the growth stage model computer program and (ii) a control treatment which was a conventional, later drained treatment. At the Gillett site there was a third treatment described below. The control treatment time of draining varied according to the farm practices (DeWitt and Gillett sites) or extension service recommendations (Stuttgart site). To accomplish the draining treatments on farm fields, steel frames were constructed and driven into the soil to accomplish a later draining than the field around a plot. These were driven into the soil to (or past) the plow layer. These frames had two 50-mm holes cut at ground level to allow access to water into the plots. At the time of draining, these holes were plugged with No. 11 rubber stoppers and the standing water removed from the area within the frames by either allowing the flood to dry up in 2005 (which occurred in that case within 1 d) or by pumping the surface water out of the frames (2006). To determine the depth of the root zone as part of Eq. [5] for predicting water available from the soil, penetrometer measurements were made over the experimental areas. Model calculations for water use [Eq. [1]–[3]), predicted soil water availability (Eq. [4]] and predicted safe RRGS for draining [Synthesis] were done for each location in each year (Table 3). All experiments were located on either a Stuttgart silt loam or a DeWitt silt loam. With one exception, the cultivar planted in the experiments was Wells (Moldenhauer et al., 2007b). The exception was the 2005 experiment at Gillett, in which the cultivar planted was Francis (Moldenhauer et al., 2007a).

An experiment was conducted in a production rice field near DeWitt, AR(34°18' N, 91°18' W) in 2006. The soil was a Stuttgart silt loam. There were two treatments in the experiment: (i) drain at the safe growth stage as determined by the growth stage model computer program and (ii) drain as the farmer drained the field—this was a control treatment without a frame with the same plot dimensions as plots of Treatment 1. The plots were 1.22 by 2.45 m. Treatment 1 plots were bordered by 14 gauge sheet metal 200 mm above the soil surface and driven into the soil 100 mm below the soil surface (the depth of the plow layer).

Two experiments were conducted in production rice fields near Gillett, AR (34°11' N, 91°41' W). The soil was a Stuttgart silt loam. The plots were 1.22 by 2.45 m bordered by 14 gauge sheet metal 200 mm above the soil surface and driven into the soil 200 mm below the soil surface (the depth of the plow layer). The sheet metal borders were installed within 2 wk of crop emergence at which the growth stage was approximately V1 to V3 (Counce et al., 2000). There were three treatments: (i) drain at the safe growth stage as determined by the growth stage model computer program with a frame, (ii) drain as the farmer drained the field with the frame around the rice, and (iii) drain as the farmer drained the field without a frame.

The experiments at Stuttgart (34°28' N, 91°25' W) had two treatments: (i) drain at the safe stage of development predicted by the growth stage model computer program and (ii) drain at 28 d after 50% heading. Harvested areas at Stuttgart were 2.5 by 9.2 m. The soil was a DeWitt silt loam. In 2006, an additional harvested area was 6 by 46 m. Each plot was bounded by its own normal earth levees. A bar ("borrow") ditch was adjacent to and between the levee and flat area planted to rice.

Plots were harvested by hand with a sickle and threshed with a stationary thresher. Rough rice harvest moisture content, yield, and milling quality were determined shortly after harvest for each plot. In 2005, grain was dried in shallow metal pans at 22°C and 50% relative humidity until equilibration moisture content (11.9–12.3%) was reached. Grain from each plot was subsequently stored in two plastic bags within each other at 7°C until it was analyzed for brown, milled, and head rice yield. In 2006, grain was dried in shallow metal pans in the lab for a few hours and then placed in two plastic bags within each other at approximately 7°C until it dried with precision drying environmental conditions. Thereafter brown, milled, and head rice yield determinations were made.

	RESULTS AND DISCUSSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
Predicted water availability with draining at RRGS from R3 through R8 are presented in Fig. 1 . The x axes are GDD after R3. The y axes are available soil water. The x axes are projected dates for projected plant water availability from each RRGS to R9 (grain maturity). Beginning at the projected dates for each successive RRGS after R3, available water after draining was predicted. When the amount of available water reached zero before projected R9, it was too early to drain. At Stuttgart in 2005, the projected safe drain RRGS was R8 (Fig. 1a). At Gillett in 2005, the projected safe drain RRGS was R6 (Fig. 1b). At all three locations in 2006, the projected safe drain RRGS was R7 (Fig. 1c,d,e). The differences in projected safe draining RRGS for 2006 (being earlier than those of 2005) are the result of projected temperatures. The temperatures in 2005 were projected to be cooler than those in 2006, this lead to longer periods between each RRGS. Since maximum water use was projected per day, this increased the projected water use per RRGS in 2005 relative to 2006.

View larger version (30K): [in this window] [in a new window]   Fig. 1. Predicted water available to the rice crop after draining for five location/year combinations of draining experiments: (a) Stuttgart in 2005, (b) Gillett in 2005, (c) DeWitt in 2006, (d) Gillett in 2006, and (e) Stuttgart in 2006. The x axis is the predicted growing degree days (GDD) accumulation for maturity or R9. For each of six stages of development, the predicted available water is from a designated stage of maturity to R9.

	CONCLUSION

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
The model predictions could lead to reduced water costs for rice production, increased water availability for soybean production, decreased tillage costs, and a reduction in the drawdown from the depleted aquifers. These considerations are critical to both the sustainability of the farm economy in the Arkansas Grand Prairie and to the future availability of economical sources of water. Further research needs to be done including testing of the model in other soils in other rice producing areas. Also, the RRGS intervals likely differ between cultivars (Watson et al., 2005). The RRGS timing datasets are needed for all widely used rice cultivars so the model can be used with a broad range of cultivars.

	ACKNOWLEDGMENTS

 
We thank Arkansas rice producers for support of this project through check-off funds. We especially thank Jody Holzhauer, John Holzhauer, Mike Dodson, James Phillips, Danny Bullock, and Walter Hillman for their cooperation. We thank Arkansas County Conservation District Board members and Terry Gray for encouragement. We thank Arkansas rice producers for financial support through check-off funds administered by the Arkansas Rice Research and Promotion Board.

	NOTES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 
All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher.

	REFERENCES

TOP NOTES ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSION REFERENCES

 

• Beyrouty, C.A., R.J. Norman, B.R. Wells, N.A. Slaton, B.C. Grigg, Y.H. Teo, and E.E. Gbur. 1996. A decade of rice root characterization studies. p. 9–20. In R.J. Norman and B.R. Wells (ed.) Rice research studies 1995. Res. Ser. 453. Arkansas Agric. Exp. Stn., Fayetteville.
• Bryant, K.J., P. Tacker, E.D. Vories, T.E. Windham, and S. Stiles. 2001. Estimating irrigation costs. Coop. Ext. Publ. FSA28-PD-5–01N. Univ. of Arkansas, Little Rock.
• Clements, J., T. Siebenmorgen, and P.A. Counce. 2003. Relationship of thermal units during grain filling to rice kernel development. p. 373–381. In R.J. Norman and J.-F. Meullenet (ed.) Rice research studies 2002. Res. Ser. 504. Arkansas Agric. Exp. Stn., Fayetteville.
• Counce, P.A. 2006. Rice–Cultivation. p. 586–589. In M. Black et al. (ed.) The encyclopedia of seeds: Science, technology and uses. CAB Int., Cambridge, MA.
• Counce, P.A., T.C. Keisling, and A.J. Mitchell. 2000. A uniform, objective and adaptive system for expressing rice development. Crop Sci. 40:436–443.[Abstract/Free Full Text]
• Counce, P.A., T.J. Siebenmorgen, and E.D. Vories. 1993. Post-heading irrigation management effects on rice grain yield and milling quality. Bull. 940. Arkansas Agric. Exp. Stn., Fayetteville.
• Counce, P.A., T.J. Siebenmorgen, E.D. Vories, and D.J. Pitt. 1990. Time of draining and harvest effects on rice grain yield and quality. J. Prod. Agric. 3:436–445.
• Custudio, E. 2002. Aquifer overexploitation: What does it mean? Hydrogeol. J. 10:252–277.
• Czarmecki, J.B., B.R. Clark, and T.B. Reed. 2003. Conjunctive-use optimization model of the Mississippi River Valley alluvial aquifer of Northeastern Arkansas. USGS Water-Resources Investigations Rep. 03–4230. USGS, Little Rock, AR.
• Davis, J.P. 2002. Exploration of the soil physical, chemical, morphological and solute transport properties of 12 soils in Arkansas. Ph.D. diss. Univ. of Arkansas, Fayetteville (Diss. Abstr. AAT 3067033).
• Dwyer, L.M., D.W. Steward, B.L. Ma, P. Neave, and D. Balchin. 1999. Guidelines for comparisons among maize maturity rating systems. Agron. J. 91:946–949.[Abstract/Free Full Text]
• Gajri, P.R., V.K. Arora, and S.S. Prihar. 2002. Tillage and edaphic environment. p. 23–64. In Tillage for sustainable cropping. Haworth Press, New York.
• Gealy, D.R., N.E. Saldain, and R.E. Talbert. 2000. Emergence of red rice (Oryza sativa L.) ecotypes under dry-seeded rice (Oryza sativa L.) culture. Weed Technol. 14:406–412.[CrossRef]
• Grigg, B.C., C.A. Beyrouty, R.J. Norman, E.E. Gbur, M.G. Hanson, and B.R. Wells. 2000. Rice responses to changes in floodwater and N timing in southern USA. Field Crops Res. 66:73–79.[CrossRef]
• Keisling, T.C., B.R. Wells, and G.L. Davis. 1984. Rice management decisions based upon thermal time basis 50°F. Ext. Tech. Bull. 1. Univ. of Arkansas, Little Rock.
• Klocke, N.L., D.E. Eisenhauer, and T.L. Bockstadter. 1991. Predicting the last irrigation. Publ. G82–608-A. Available at http://www.p2pays.org/ref/20/19760.htm#LASTIRRIG [revised Aug. 1991; cited 10 Sept. 2008; verified 30 Oct. 2008]. Coop. Ext., Inst. of Agric. and Natural Resources, Univ. of Nebraska, Lincoln.
• Lage, M., A. Bamouh, M. Karrou, and M. El Mourid. 2003. Estimation of rice evapotranspiraton using a rice microlysimeter technique and comparison with FAO Penman-Monteith and Pan evaporation methods under Moroccan conditions. Agronomie 23:625–631.
• McCauley, G.N., and M.O. Way. 2002. Drain and harvest timing affects on rice grain drying and whole-milled grain. Field Crops Res. 74:163–172.
• Moldenhauer, K.A.K., J.W. Gibbons, F.N. Lee, J.L. Bernhardt, C.E. Wilson, R.D. Cartwright, M.M. Anders, R.J. Norman, N.A. Slaton, M.M. Blocker, A.C. Tolbert, K. Taylor, and J.M. Bulloch. 2007a. Registration of Francis rice. Crop Sci. 47:443–444.[Free Full Text]
• Moldenhauer, K.A.K., F.N. Lee, J.L. Bernhardt, R.J. Norman, N.A. Slaton, C.E. Wilson, M.M. Anders, R.D. Cartwright, and M.M. Blocker. 2007b. Registration of Wells rice. Crop Sci. 47:442–443.[Free Full Text]
• Noldin, J.A., J.M. Chandler, and G.N. McCauley. 2006. Seed longevity of red rice ecotypes buried in soil. Planta Daninha 24:611–620.
• Popp, M., P. Manning, P. Counce, and T. Keisling. 2005. Rice-soybean rotations: Opportunities for enhancing whole farm profits or water savings. Agric. Syst. 86:223–238.
• Renaud, F., J.A. Ferguson, H.D. Scott, and D.M. Miller. 2000. Estimation of season rice evapotranspiration. p. 56–62 In R.J. Norman et al. (ed.) Rice research studies 1999. Res. Ser. 476. Arkansas Agric. Exp. Stn., Fayetteville.
• Scott, H.D., J.A. Ferguson, L. Hanson, T. Fugitt, and E. Smith. 1998. Agricultural water management in the Mississippi River Delta region of Arkansas. Bull. 959. Arkansas Agric. Exp. Stn., Fayetteville.
• Sharma, P.K., G. Pantuwan, K.T. Ingram, and S.K. De Datta. 1994. Rainfed lowland rice roots: Soil and hydrological effects. p. 55–66. In G.J.D. Kirk. Rice roots: Nutrient and water use. IRRI, Manila, Philippines.
• Siebenmorgen, T.J., R.C. Bautista, and P.A. Counce. 2007. Optimal harvest moisture contents for maximizing milling quality of long- and medium-grain rice cultivars. Appl. Eng. Agric. 23:517–527.
• Sophocleous, M. 2000. From safe yield to sustainable development of water resources—The Kansas experience. J. Hydrol. 235:27–43.
• Watkins, K.B., J.L. Hill, and M.M. Anders. 2006. Impact of alternative well pumping depths on Arkansas rice farm profitability. p. 77. In Proc. of the 31st Rice Technical Working Group Meet., The Woodlands, TX. 26 Feb–1 Mar. 2006. Louisiana State Univ. Agric. Ctr., Louisiana Exp. Stn., Rice Res. Ctr., Crowley.
• Watkins, B., J. Hill, and C.E. Wilson, Jr. 2007. Estimating 2007 costs of production, rice, silt loam soils., Eastern Arkansas. Ext. Tech. Bull. AG-924–11–05, November 2005. Univ. of Arkansas, Little Rock.
• Watson, N.T., P.A. Counce, and T.J. Siebenmorgen. 2005. Growth stages of 12 rice cultivars (Oryza sativa L.) expressed in GDD thermal heat units. p. 56–62. In R.J. Norman et al. (ed.) Rice research studies 2004. Res. Ser. 529. Arkansas Agric. Exp. Stn., Fayetteville.
• Yoshida, S. 1981. Fundamentals of rice crop science. IRRI, Manila, The Philippines.

This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Search for Related Content PubMed Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Agricola Articles by Counce, P. A. Articles by Siebenmorgen, T. J. Related Collections Other Crop Management Crop Models Other Models Irrigation