Nitrogen recommendations for Upland cotton (Gossypium hirsutum L.) in the western USA are based on spring soil NO−3–N tests. In‐season monitoring of plant N status is another approach. Our primary objective was to test spectral reflectance and chlorophyll meter measurements as in‐season N decision aids for irrigated cotton, and to compare these with soil test‐based N management. The secondary objective was to determine the fate of 15N as affected by N management and irrigation modes. Urea ammonium nitrate was applied with low energy precision (LEPA) center‐pivot, surface drip, and subsurface drip irrigation. Microplots received 3 atom% 15N. Soil test N application was based on 0‐ to 60‐cm soil NO−3–N and 1400 kg lint ha−1 expected yield. Thirty‐four kilograms of N per hectare was applied when green vegetative index (GVI) or chlorophyll meter readings relative to well‐fertilized plots were <0.95. Lint yield responded to N at Lubbock in 2000 and 2001, but not at Ropesville. Nitrogen applied with in‐season monitoring in 2000 at both sites was 34 to 101 kg N ha−1 less than soil test N application of 134 kg ha−1, with similar yields. In Lubbock, 2001 lint yields were near the expected yield, and in three of four cases, N applications with in‐season monitoring equaled soil test N applications of 101 kg ha−1 Nitrogen‐15 recovery in plants ranged from 19 to 38%, and was affected by N management in two of three site‐years, but not by irrigation. This study indicates that basing N applications on in‐season monitoring can reduce N applications in low yielding seasons and match the yield potential in high‐yielding seasons.

Precision agriculture technologies offer an opportunity to vary production inputs within a field. Variable rate application offers the potential to increase production efficiency and minimize potential adverse environmental effects of agricultural chemicals. As an initial step in the development of precision agriculture technologies for cotton, studies are needed to document variability of cotton. The primary objective of this study was to document variability of yield and quality of irrigated cotton within and across three growing seasons. This study was conducted on a 5.3 ha irrigated field located at the Erskine Research Farm at Texas Tech University, Lubbock, TX. The crop was grown under a conventional tillage system with a 1.0 m row spacing. With the exception of sample collection, the field was managed traditionally with respect to production inputs. A grid system (57 points) was established on 30.5 m (approximately 0.1 ha) intervals. Production of fruiting 351 ORDER REPRINTS sites, fruit retention, lint yield, fiber length, strength, micronaire, and gross revenue were estimated for each grid point. Soil chemical and physical properties were also determined for each grid point. Highest variability was observed for lint yield and production of fruiting sites, and lowest variability was observed for lint quality parameters. Yield was correlated to production of fruiting sites each season. Nitrate concentrations were highly variable, and yield was negatively correlated to nitrogen (N) in 1997. This suggests that variable application of N may be a viable management option in the future. Yield was positively correlated to calcium (Ca), pH, and CEC in 1997. Yield variability was correlated across growing seasons. Gross revenues were quite variable, due primarily to yield variability.

No abstract

No abstract

The extremely toxic protein, ricin, is derived from castor beans and is a potential terrorist weapon. Adsorption to clays might minimize the environmental persistence and toxic effects of this toxin. Ricin adsorption to clay minerals was measured using batch adsorption isotherms. Enzyme-linked immunoassay methods were used to quantify aqueous ricin concentrations. Montmorillonite, sepiolite and palygorskite effectively adsorbed ricin from aqueous solutions and yielded mostly Langmuir-type isotherms. The monolayer adsorption capacity from a Langmuir equation fit at pH 7 was 444 g ricin/kg for montmorillonite (SWy-2), but was only 5.6 g ricin/kg for kaolinite (KGa-1b). Monolayer capacities for sepiolite (SepSp-1) and palygorskite (PFl-1) at pH 7 were 59.2 and 58.1 g ricin/kg. The high-charge montmorillonite (SAz-1) effectively adsorbed ricin at pH 7, but yielded a linear isotherm with K = 5530 L/kg. At pH 5, both montmorillonites (SWy-2 and SAz-1) yielded Langmuir-type isotherms with monolayer capacities of 694 and 641 g ricin/kg. Clay samples with higher cation exchange capacities generally adsorbed more ricin, but adsorption also followed specific surface area. X-ray diffraction of <2 μm SWy-2 treated with 470 g ricin/kg indicated expansion up to 34.6 Å at buffered pHs of 4 and 7, but not at pH 10. Furthermore, ricin adsorption was greatest at pH 4 and 7, but minimal at pH 10. Treatment with 1.41 kg of purified ricin/kg clay at pH 5 yielded a 35.3 Å peak and adsorption of ~1.2 kg ricin/kg. Similar treatment with lower-purity ricin yielded less expansion and lower adsorption. The 35.3 Å peak interpreted either as a d002 or d001 reflection indicates a 70.6 Å or a 35.3 Å ricin/SWy-2 complex. This implies that adsorption and air drying have compressed interlayer ricin molecules by 18 to 65%. Effective ricin adsorption by montmorillonite suggests that it could be used to minimize the toxic effects of dispersed ricin.

Student performance in an introductory soils class, Principles and Practices of Soils, at Texas Tech University was evaluated for 15 semesters as a function of several high school and college variables on course success. The variables evaluated were SAT score, percentile rank in high school class, high school chemistry background, major, gender, and academic semester in which the class was taken. Two variables, semester the class was taken and high school chemistry, were not significantly related to the final grade achieved in the course. All other variables were significant. Stepwise multiple regression indicated that each participant's high school ranking and SAT score accounted for 19.4% of the variation. A one‐unit decrease in percentile rank resulted in a 1.5 increase in final grade achieved, holding the other variables constant. Similarly, SAT score contributed statistically to the regression equation, but to a much lesser extent.

No abstract