Characteristics and Production Costs of U.S. Rice Farms

Foreman, Linda F.; Livezey, Janet S.

doi:10.2139/ssrn.747504

Cited by 33 publications

(6 citation statements)

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“…These results again demonstrated the difficulty in identifying the genotype with the highest gross income for a specific location or group of locations and the importance of understanding the effect of GE interaction on gross income. Livezey and Foreman (2004) identified the ricegrowing regions in the United States and estimated their respective total operating and ownership costs. Total costs were estimated to be $846 ha…”

Section: Resultsmentioning

confidence: 99%

Use of gross income as a measure of productivity in rice breeding

Samonte

Wilson²,

Tabien³

et al. 2008

Can. J. Plant Sci.

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Rice breeders consider grain yield and milled rice percentages in developing cultivars, but usually do not consider gross income. This study’s objectives were to identify rice genotypes that produced high and stable expected gross incomes using genotype plus genotype × environment (GGE) biplot analysis. Uniform Regional Rice Nursery data on 47 long-grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during 2001 to 2003 were analyzed. Gross income of each genotype was estimated based on grain yield, milled rice percentages, market prices, and direct and counter-cyclical payments. Based on GGE biplot analysis, the genotypes with the highest yield and highest gross income for the main crop were different in 12 out of 13 environments. RU0103184, Francis, and RU0003178 were the genotypes with the highest gross income in six, four, and three environments, respectively. Rice breeders should consider gross income as a selection criterion in the release of new cultivars. Key words: Rice, GGE, GE, breeding, gross income

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Section: Resultsmentioning

confidence: 99%

Use of gross income as a measure of productivity in rice breeding

Samonte

Wilson²,

Tabien³

et al. 2008

Can. J. Plant Sci.

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“…This model simulates a one time chemical pulse to a defined rice field environment. The simulation begins with an etofenprox application made aerially to a 247 acre area (90% of acreage flooded; flooded soil is sediment in model), representative of a large family rice farm . Model inputs, such as environmental parameters, chemical properties and reaction rates, are summarized in the Supporting Information.…”

Section: Methodsmentioning

confidence: 99%

“…The simulation begins with an etofenprox application made aerially to a 247 acre area (90% of acreage flooded; flooded soil is sediment in model), representative of a large family rice farm. 13 Model inputs, such as environmental parameters, chemical properties and reaction rates, are summarized in the Supporting Information. The field is assumed to have no to little vegetation density, as etofenprox is applied one to seven days after field flooding and seeding.…”

Section: ' Introductionmentioning

confidence: 99%

Soil and Glass Surface Photodegradation of Etofenprox under Simulated California Rice Growing Conditions

Vasquez

Cahill

Tjeerdema

2011

J. Agric. Food Chem.

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Photolysis is an important degradation process to consider when evaluating a pesticide's persistence in a rice field environment. To simulate both nonflooded and flooded California rice field conditions, the photolytic degradation of etofenprox, an ether pyrethroid, was characterized on an air-dried rice soil and a flooded rice soil surface by determination of its half-life (t(1/2)), dissipation rate constant (k) and identification and quantitation of degradation products using LC/MS/MS. Photodegradation was also characterized on a glass surface alone to rule out confounding soil factors. Measured photolytic dissipation rates were used as input parameters into a multimedia environmental fate model to predict etofenprox persistence in a rice field environment. Photolytic degradation proceeded at a faster rate (0.23/day, t(1/2) = 3.0 days) on the flooded soil surface compared to the air-dried surface (0.039/day, t(1/2) = 18 days). Etofenprox degradation occurred relatively quickly on the glass surface (3.1/day, t(1/2) = 0.23 days or 5.5 h) compared to both flooded and air-dried soil layers. Oxidation of the ether moiety to the ester was the major product on all surfaces (max % yield range = 0.2 ± 0.1% to 9.3 ± 2.3%). The hydroxylation product at the 4' position of the phenoxy phenyl ring was detected on all surfaces (max % yield range = 0.2 ± 0.1% to 4.1 ± 1.0%). The air-dried soil surface did not contain detectable residues of the ester cleavage product, whereas it was quantitated on the flooded soil (max % yield = 0.6 ± 0.3%) and glass surface (max % yield = 3.6 ± 0.6%). Dissipation of the insecticide in dark controls was significantly different (p < 0.05) compared to the light-exposed surfaces indicating that degradation was by photolysis. Laboratory studies and fate model predictions suggest photolysis will be an important process in the overall degradation of etofenprox in a rice field environment.

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“…Moreover, the rice growing seasons vary by region due to various environmental conditions and farmers' decisions. We divided the entire study area into the following four regions based on their rice phenology and growth environments: Arkansas Grand Prairie (AP), Mississippi Delta (MD), Gulf Coast (GC), and Sacramento Valley (SV) [44,45]. Unlike other parts of the world where rice production is divided between irrigated and non-irrigated fields, all U.S. rice is produced in irrigated fields [43].…”

Section: Materials 21 Study Areamentioning

confidence: 99%

Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series

et al. 2022

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Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.

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Characteristics and Production Costs of U.S. Rice Farms

Cited by 33 publications

References 1 publication

Use of gross income as a measure of productivity in rice breeding

Use of gross income as a measure of productivity in rice breeding

Soil and Glass Surface Photodegradation of Etofenprox under Simulated California Rice Growing Conditions

Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series

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