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For centuries the Straits of Malacca, a narrow waterway between the Malay peninsula and the island of Sumatra, has been both a major conduit for long distance trade between Asia and the West and one of the most dangerous areas for commercial shipping.

Mobile enterprises are a significant part of the rural market system in Central Province, Kenya. This pattern differs from West African periodic markets. Existing central place and classical firm-location models based on West African examples are inadequate to explain completely the enterprise mobility patterns in Central Province and perhaps elsewhere in East Africa. Historical factors, increase of small part-time and mobile vendors, and their limited mobility account for the distinctiveness of this East African pattern. The backward-sloping supply curve is used to analyze mobile enterprises in Central Province.

L'article présente les résultats d'une enquête effectuée en 1977 auprès de 852 entreprises rurales non-agricoles. Ce secteur joue un grand rôle dans l'économie de la Province centrale. Ses caractéristiques fonctionnelles sont examinées à différents niveaux, dont celui de l'emploi. Le secteur rural non-agricole regroupe un large éventail d'activités productives et de services, dont plusieurs ne sont pas traditionnells, et entretient de multiples rapports économiques tant à l'échelon local qu'avec le réseau urbain. Même s'ils ne sont pas essentiels à son fonctionnement, ces rapports sont suffisament importants pour intervenir dans le processus du développement économique. Les auteurs analysent enfin la place des rapports de parenté dans l'évolution des activités rurales non-agricoles; ils constatent que les conditions d'accès à ce secteur, combinées aux liens de parenté, favorisent l'émergence de la petite bourgeoisie.

Drastic increases in the cost of N fertilizer and increased public scrutiny have encouraged development and implementation of improved N management practices. This study evaluated the relationship between corn (Zea mays L.) grain yield and early season normalized difference vegetation index (NDVI) sensor readings using the GreenSeeker sensor. The relationships between grain yield and several predictor variables were determined using linear and nonlinear regression analysis. Categorizing NDVI measurement by leaf stage indicated that growth stage was critical for predicting grain yield potential. Poor exponential relationships existed between NDVI from early sensor measurements (V6-V7 leaf stage) and grain yield. By the V8 stage, a strong relationship (R2 = 0.77) was achieved between NDVI and grain yield. Later sensor measurements (V9 and later) failed to distinguish variation in green biomass as a result of canopy closure. Normalizing the NDVI with GDD (growing degree days) did not significantly improve yield potential prediction (R2 = 0.73), but broadened the yield potential prediction equation to include temperature and allowed for adaptation into various climates. Sensor measurements at the range of 800 to 1000 GDD resulted in a significant exponential relationship between grain yield and NDVI (R2 = 0.76) similar to the V8 leaf stage categorization. Categorizing NDVI by GDD (800-1000 GDD) extended the sensing time by two additional leaf stages (V7-V9) to allow a practical window of opportunity for sidedress N applications. This study showed that yield potential in corn could be accurately predicted in season with NDVI measured with the GreenSeeker sensor.

As research intensifies on developing precision agricultural practices for corn (Zea mays L.) production, an important component will be to identify the scale at which these practices should be implemented. We hypothesized that optical sensing can be used to measure individual corn plant biomass and N uptake. A 3-yr study was conducted at three locations in Oklahoma. Optical sensor readings of normalized difference vegetation index (NDVI) and plant height measurements were collected on individual corn plants at various growth stages ranging from V8 (collar of eighth leaf unfolded) to VT (last branch of the tassel is completely visible) and correlated with individual plant biomass, forage yield per unit area occupied by the plant, and N uptake of that plant. Individual plant height measurement, collected before reproductive growth, was a good predictor of plant biomass across the six site years of the study (r2 = 0.81). The index of NDVI x plant height provided the highest correlation with by-plant forage yield on an area basis. Optical sensor and plant height measurements collected at the V8 to V10 (collar of 10th leaf unfolded) growth stage can distinguish individual plants and provide information as to their biomass accumulation and N uptake. This research demonstrates that by-plant information can be collected and used to direct high resolution N applications. The index, NDVI x plant height, may be used to refine midseason fertilizer N rates based on expected N removal and by-plant measurements at or before V10.

Improving crop management inputs with remote sensing devices is an emerging technology. This study documented the progression of the normalized difference vegetative index (NDVI) during the life cycle of corn (Zea mays L.), evaluated the spatial variability of corn growth in terms of the CV (calculated from NDVI readings), and documented the relationships between NDVI, CV (calculated from NDVI), and grain and biomass yields and plant density. Four rows, 30 m in length, from two locations during 2 yr were randomly selected for this study. An optical sensor was used to collect NDVI readings at multiple growth stages during the life cycle of corn. The NDVI increased with progression of vegetative growth stages until V10, where a plateau was encountered, followed by a decline in NDVI after the VT growth stage. Coefficient of variation data from the NDVI readings revealed two dominant peaks during the life cycle of corn, one between the V6 and V8 growth stages and the second during the late reproductive growth stages. The CV data illustrated that the greatest variation expressed by corn during the vegetative growth stages was between the V6 and V8 growth stages. The highest correlation of NDVI with corn grain yield was found at the V7 to V9 growth stages; likewise, CV and plant density were also more highly correlated from V7 to V9. The CV from NDVI readings was highly correlated with grain and biomass yields at all growth stages.

Corn (Zea mays L.) grain yields are known to vary from plant to plant, but the extent of this variability across a range of environments has not been evaluated. This study was initiated to evaluate by-plant corn grain yield variability over a range of production environments and to establish the relationships among mean grain yield, standard deviation, coefficient of variation, and yield range. A total of forty-six 8- to 30-m corn transects were harvested by plant in Argentina, Mexico, Iowa, Nebraska, Ohio, Virginia, and Oklahoma from 2002 to 2004. By-plant corn grain yields were determined, and the average individual plant yields were calculated. Over all sites in all countries and states, plant-to-plant variation in corn grain yield averaged 2765 kg ha(-1) (44.1 bu ac(-1)). At the sites with the highest average corn grain yield (11 478 and 14 383 kg ha(-1), Parana Argentina, and Phillips, NE), average plant-to-plant variation in yield was 4211 kg ha(-1) (67 bu ac(-1)) and 2926 kg ha(-1) (47 bu ac(-1)), respectively. As average grain yields increased, so did the standard deviation of the yields obtained within each row. Furthermore, the yield range (maximum corn grain yield minus the minimum corn grain yield per row) was found to increase with increasing yield level. Regardless of yield level, plant-to-plant variability in corn grain yield can be expected and averaged more than 2765 kg ha(-1) over sites and years. Averaging yield over distances >0.5 m removed the extreme by-plant variability, and thus, the scale for treating other factors affecting yield should be less than 0.5 m. Methods that homogenize corn plant stands and emergence may decrease plant-to-plant variation and could lead to increased grain yields.

A new technique is presented for the rapid, high-resolution identification and quantification of multiple trace gases above soils, at concentrations down to 0.01 µL L⁻¹ (10 ppb). The technique, selected ion flow tube mass spectrometry (SIFT–MS), utilizes chemical ionization reagent ions that react with trace gases but not with the major air components (N₂, O₂, Ar, CO₂). This allows the real-time measurement of multiple trace gases without the need for preconcentration, trapping, or chromatographic separation. The technique is demonstrated by monitoring the emission of ammonia and nitric oxide, and the search for volatile organics, above containerized soil samples treated with synthetic cattle urine. In this model system, NH₃ emissions peaked after 24 h at 2000 nmol m⁻² s⁻¹ and integrated to approximately 7% of the urea N applied, while NO emissions peaked about 25 d after urine addition at approximately 140 nmol m⁻² s⁻¹ and integrated to approximately 10% of the applied urea N. The monitoring of organics along with NH₃ and NO was demonstrated in soils treated with synthetic urine, pyridine, and dimethylamine. No emission of volatile nitrogen organics from the urine treatments was observed at levels >0.01% of the applied nitrogen. The SIFT method allows the simultaneous in situ measurement of multiple gas components with a high spatial resolution of <10 cm and time resolution <20 s. These capabilities allow, for example, identification of emission hotspots, and measurement of localized and rapid variations above agricultural and contaminated soils, as well as integrated emissions over longer periods.