Juan Landivar

Influence of low-weight seeds and motes on the fiber properties of other cotton seeds

Abstract Suboptimal growth conditions can hinder cotton fiber growth and development. Bolls were selected from cotton plants ( Gossypium hirsutum L.; Deltapine 50, 51) grown in Texas over a 3 yr period. Fiber samples from seeds located in the middle of the boll were analyzed using the advanced fiber information system (AFIS). Motes are developmentally arrested seeds and their associated fiber. By definition, motes cannot germinate. Motes were divided into two categories — short-fiber and long-fiber — where fiber from long-fiber motes was one half the length of fibers on normal seeds. Large numbers of short-fiber motes per boll did not have a detrimental effect on the fiber quality of the middle seeds in a boll. Large numbers of long-fiber motes per boll reduced the extent of secondary wall deposition in fibers from middle seeds, while small numbers of low-weight seeds or long-fiber motes per boll did not affect the fiber quality of middle seeds. The identification of sources of poor quality fiber facilitates prediction of dyeing irregularities ultimately benefiting producers, processors and consumers.

Physiological Simulation of Cotton Growth and Yield

Scientists early in the twentieth century sought ways to describe and predict plant growth. Gregory (1917) and Blackman (1919) developed methodology called “growth analysis” to describe net assimilation rate, and compared dry matter accumulation to compound interest. By the middle of the century, leaf area index and light interception were recognized as important parameters for estimating photosynthesis in crop stands and were thus related to canopy dry matter growth. During the 1960s and 1970s, leaf and canopy photosynthesis were described using commercially available gx analyzers, radiation sensors, and other devices for measuring environmental conditions that facilitated studies remarkably. There were extensive debates regarding leaf angles, radiation attenuation, carbon accumulation, and maintenance and growth respiration. Several laboratories became interested in relating information on environmental conditions to photosynthesis, plant growth, and harvestable yield. The development of simulation models of the various processes had become feasible. The objectives and validation methods varied widely among crop modelers.

Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery

Abstract. This paper explores the potential of using unmanned aircraft system (UAS)-based visible-band images to assess cotton growth. By applying the structure-from-motion algorithm, the cotton plant height (ph) and canopy cover (cc) information were retrieved from the point cloud-based digital surface models (DSMs) and orthomosaic images. Both UAS-based ph and cc follow a sigmoid growth pattern as confirmed by ground-based studies. By applying an empirical model that converts the cotton ph to cc, the estimated cc shows strong correlation (R2=0.990) with the observed cc. An attempt for modeling cotton yield was carried out using the ph and cc information obtained on June 26, 2015, the date when sigmoid growth curves for both ph and cc tended to decline in slope. In a cross-validation test, the correlation between the ground-measured yield and the estimated equivalent derived from the ph and/or cc was compared. Generally, combining ph and cc, the performance of the yield estimation is most comparable against the observed yield. On the other hand, the observed yield and cc-based estimation produce the second strongest correlation, regardless of the complexity of the models.

The Cotton Fiber Property Variability Continuum from Motes Through Seeds

Cotton fiber quality at the bale level is a composite of all the constitutive fibers in the bale. Bales contain fibers from both mature seeds and motes (developmentally arrested seeds). The degree of variability shown in the fiber properties of seeds and motes serves as an indicator of the amount of variability in a bale. Cotton (Gossyium hirsutum L.) has been collected from machine harvested fields, and fibers have been removed by hand or ginned with a small laboratory saw gin and analyzed with the Zellweger-Uster advanced fiber information system (AFIS). Distribution of ginned mote and seed weights are similar for three cotton varieties, but composite fiber properties are different. Early termination of embryo growth results in short-fiber motes. The degree of secondary wall deposition for short-fiber motes shows that the capacity for cell wall synthesis is not terminated with the termination of embryo growth. Cotton samples are categorized by ginned mote and seed weights. The cotton varieties with the most mature fibers also have the most mature mote fibers. As ginned seed weight increases, fiber maturity increases. Fiber perimeter values fluctuate for motes but remain constant once a ginned seed weight of 56 mg is reached.

Effects of growth environment on cotton fiber properties and motes, neps, and white speck frequency

A number of preharvest and postharvest factors alter textile quality, including cotton variety, growth environment, harvest method, lint cleaning, and combing. In this work, the impact of three planting dates in 1997 and 1999 on fiber properties, mote, neps, and white specks is monitored. Cotton is hand picked, ginned on a small laboratory gin, and processed in a mini-spinning facility. The number of motes per gram of seed cotton does not correspond to changes in nep or white speck frequency. Also, a decrease in the mean micronafis values (which corresponds to micronaire) does not correlate with increases in white speck frequency. The range of 0 values is larger for 1999 cotton than for 1997 cotton. We propose th the distribution of 0 values is important in predicting white speck potential, and that the amount of very mature fibers may be just as important as the amount of immature fibers.

Efeito de cultura de cobertura e de rotação de cultura no armazenamento de água do solo e no rendimento de sorgo

Crop rotation and cover crop can be important means for enhancing crop yield in rainfed areas such as the lower Coastal Bend Region of Texas, USA. A trial was conducted in 1995 as part of a long-term cropping experiment (7 years) to investigate the effect of oat (Avena sativa L.) cover and rotation on soil water storage and yield of sorghum (Sorghum bicolor L.). The trial design was a RCB in a split-plot arrangement with four replicates. Rotation sequences were the main plots and oat cover crop the subplots. Cover crop reduced sorghum grain yield. This effect was attributed to a reduced concentration of available soil N and less soil water storage under this treatment. By delaying cover termination, the residue with a high C/N acted as an N sink through competition and/or immobilization instead of an N source to sorghum plants. Crop rotation had a significantly positive effect on sorghum yield and this effect was attributed to a significantly larger amount of N concentration under these rotation sequences.

A ground based platform for high throughput phenotyping

Abstract The objective of this effort was to evaluate current commercially-available sensor technology (three sonic ranging and two NDVI sensors) for use in a ground-based platform for plant phenotyping and crop management decisions. The Global Positioning System (GPS) receiver from Trimble provided a high level of accuracy during our tests. Normalized Difference Vegetation Index (NDVI) data collected using the GreenSeeker sensors were more consistent and presented less variability when compared to the Decagon SRS sensor. The consistency could be due to the GreenSeeker system averaging readings of more rows. The tests also indicated that although sonic ranging sensor technology may be employed to obtain average plant height estimates, the technology is still a limiting factor for high-accuracy measurements at the plant level.

Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes

Abstract Recent advances in molecular breeding and bioinformatics have greatly accelerated the screening of large sets of genotypes. Development of field-level phenotyping, however, still lags behind and is considered by many as the main bottleneck to improved efficiency in breeding programs. Unmanned Aerial System (UAS) and sensor technology available today enables collection of data at high spatial and temporal scales, previously unobtainable using traditional airborne remote sensing technologies. Here, we propose an UAS-assisted high throughput phenotyping framework for cotton ( Gossypium hirsutum L.) genotype selection. UAS data collected on July 24, 2015 were used to calculate canopy cover, and UAS data collected on August 5, 2015 were used to extract open boll related phenotypic features including number of open bolls, average area of open bolls, average diameter of open bolls, perimeter of open bolls, perimeter to area ratio. Using the extracted features, a sequential selection procedure was performed on a population of 144 entries. Entries selected from the proposed framework were compared to the highest yielding entries determined by mechanical harvest results. Experimental results indicated that the selection process increased minimum and average lint yield of the remaining population by 7.4 and 10%, respectively, and UAS-selected entries and genotypes matched 80 and 73%, respectively, the same lists ranked by actual field harvest measurements.

Phenotyping of sorghum panicles using unmanned aerial system (UAS) data

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days for precision agriculture and High Throughput Phenotyping (HTP). Attributes of sorghum panicle, especially, are critical information to assess overall crop condition, irrigation, and yield estimation. In this study, it is proposed a method to extract phenotypes of sorghum panicles using UAS data. UAS data were acquired with 85% overlap at an altitude of 10m above ground to generate super high resolution data. Orthomosaic, Digital Surface Model (DSM), and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the imagery from UAS. Sorghum panicles were identified from orthomosaic and DSM by using color ratio and circle fitting. The cylinder fitting method and disk tacking method were proposed to estimate panicle volume. Yield prediction models were generated between field-measured yield data and UAS-measured attributes of sorghum panicles.

Cotton growth modeling using unmanned aerial vehicle vegetation indices

Unmanned aerial vehicle (UAV) images have great potential for agricultural researches because of their high spatial and temporal resolutions. However, most UAV researches in the agriculture field have adopted vegetation indices without second derivative parameters related with a growth model. In addition, visible band vegetation indices in UAV researches have not been explored in detail despite of their importance in UAV application. In this study, three RGB vegetation indices that showed good performance in previous studies are adopted and growth modeling using time series vegetation indices is proposed. In addition, growth model-based second derivatives are extracted for crop growth analysis. R squares of the proposed method from three RGB vegetation indices were 0.8–0.9 and excessive green vegetation index (ExG) showed the best accuracy.