Manipulation of crowding stress in corn.

Competition in corn crop was studied through manipulation of crowding (density and spacing) and assimilate supply in several experiments conducted in 1986-1988. Growth and yield responses of 2 hybrids differing in maturity period and prolificacy were studied in a wide range of densities. Under favorable condition, Agway 584S (single-ear, late maturity) out yielded Cornell 281 (multiple-ear, early maturity). Higher yield in Agway 584S was attributed to heavier kernels compared to Cornell 281, which resulted from a longer grain filling duration. Growth analysis indicated that crop growth rate as well as net assimilation rate were greater in Cornell 281 than in Agway 584S. Higher crop growth rate in Cornell 281, however, was directed toward tiller production with no fertile ears. Agway 584S maturing later had longer growth duration contributing to its higher yield than Cornell 281. Intensity of competition was quantified by comparing the grain yield and its components to isolated plants. Yield per plant decreased 75% and 80% compared to isolated plants in Agway 584S and Cornell 281 respectively, as density increased from 3 to 12 plants m\sp{-2}. Number of kernels per row in Agway 584S and number of productive ears per plant in Cornell 281 were found to be the most sensitive yield components. The timing of competitive stress was investigated by removal of alternate plants at different stages of growth. Most of the competition within the corn canopy occurred during the period between vegetative stage and anthesis. Adjustment in grain yield in response to releasing the competition pressure occurred primarily through increase in kernel number in Agway 584S and number of productive ears per plant in Cornell 281. Artificial shading (50% light reduction) showed that reduction in yield in high densities is due to reduction in photosynthesis rate caused by mutual shading and also by lengthening of the intervals between anthesis and silking. Density-light relationships in six corn hybrids having different leaf orientation indicated that hybrids with upright leaves had some yield advantages over horizontal leaf hybrids in favorable climatological conditions when high densities are used. Results supported the model proposed by Duncan (Crop Sci., 1984) that a linear relationship exists between the logarithm of yield per plant and crowding

Model of Yield Response of Corn to Plant Population and Absorption of Solar Energy

Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha−1 and g plant−1) on plant population (plants m−2). Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L.) grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Ym (Mg ha−1) for maximum yield at high plant population and c (m2 plant−1) for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, xc = 1/c (plants m−2). The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of xc were very similar for the three field studies with the same crop species

QTL Analysis of Shading Sensitive Related Traits in Maize under Two Shading Treatments

During maize development and reproduction, shading stress is an important abiotic factor influencing grain yield. To elucidate the genetic basis of shading stress in maize, an F2:3 population derived from two inbred lines, Zhong72 and 502, was used to evaluate the performance of six traits under shading treatment and full-light treatment at two locations. The results showed that shading treatment significantly decreased plant height and ear height, reduced stem diameter, delayed day-to-tassel (DTT) and day-to-silk (DTS), and increased anthesis-silking interval (ASI). Forty-three different QTLs were identified for the six measured traits under shading and full light treatment at two locations, including seven QTL for plant height, nine QTL for ear height, six QTL for stem diameter, seven QTL for day-to-tassel, six QTL for day-to-silk, and eight QTL for ASI. Interestingly, three QTLs, qPH4, qEH4a, and qDTT1b were detected under full sunlight and shading treatment at two locations simultaneously, these QTL could be used for selecting elite hybrids with high tolerance to shading and high plant density. And the two QTL, qPH10 and qDTS1a, were only detected under shading treatment at two locations, should be quit for selecting insensitive inbred line in maize breeding procedure by using MAS method

Site-specific seeding using multi-sensor and data fusion techniques : a review

Site-specific seeding (SSS) is a precision agricultural (PA) practice aiming at optimizing seeding rate and depth, depending on the within field variability in soil fertility and yield potential. Unlike other site-specific applications, SSS was not adopted sufficiently by farmers due to some technological and practical challenges that need to be overcome. Success of site-specific application strongly depends on the accuracy of measurement of key parameters in the system, modeling and delineation of management zone maps, accurate recommendations and finally the right choice of variable rate (VR) technologies and their integrations. The current study reviews available principles and technologies for both map-based and senor-based SSS. It covers the background of crop and soil quality indicators (SQI), various soil and crop sensor technologies and recommendation approaches of map-based and sensor-based SSS applications. It also discusses the potential of socio-economic benefits of SSS against uniform seeding. The current review proposes prospective future technology synthesis for implementation of SSS in practice. A multi-sensor data fusion system, integrating proper sensor combinations, is suggested as an essential approach for putting SSS into practice

A computational method for prediction of xylanase enzymes activity in strains of Bacillus subtilis based on pseudo amino acid composition features.

Xylanases are hydrolytic enzymes which based on physicochemical properties, structure, mode of action and substrate specificities are classified into various glycoside hydrolase (GH) families. The purpose of this study is to show that the activity of the members of the xylanase family in the specified pH and temperature conditions can be computationally predicted. The proposed computational regression model was trained and tested with the Pseudo Amino Acid Composition (PseAAC) features extracted solely from the amino acid sequences of enzymes. The xylanases with experimentally determined activities were used as the training dataset to adjust the model parameters. To develop the model, 41 strains of Bacillus subtilis isolated from field soil were screened. From them, 28 strains with the highest halo diameter were selected for further studies. The performance of the model for prediction of xylanase activity was evaluated in three different temperature and pH conditions using stratified cross-validation and jackknife methods. The trained model can be used for determining the activity of newly found xylanases in the specified condition. Such computational models help to scale down the experimental costs and save time by identifying enzymes with appropriate activity for scientific and industrial usage. Our methodology for activity prediction of xylanase enzymes can be potentially applied to the members of the other enzyme families. The availability of sufficient experimental data in specified pH and temperature conditions is a prerequisite for training the learning model and to achieve high accuracy