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Journal articles on the topic 'Phenology Mathematics'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Liu, Jun, Xi Yang, Hao Long Liu, and Zhi Qiao. "Algorithms and Applications in Grass Growth Monitoring." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/508315.

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Monitoring vegetation phonology using satellite data has been an area of growing research interest in recent decades. Validation is an essential issue in land surface phenology study at large scale. In this paper, double logistic function-fitting algorithm was used to retrieve phenophases for grassland in North China from a consistently processed Moderate Resolution Spectrodiometer (MODIS) dataset. Then, the accuracy of the satellite-based estimates was assessed using field phenology observations. Results show that the method is valid to identify vegetation phenology with good success. The phenophases derived from satellite and observed on ground are generally similar. Greenup onset dates identified by Normalized Difference Vegetation Index (NDVI) and in situ observed dates showed general agreement. There is an excellent agreement between the dates of maturity onset determined by MODIS and the field observations. The satellite-derived length of vegetation growing season is generally consistent with the surface observation.

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Yurk, Brian P., and James A. Powell. "Modeling the Evolution of Insect Phenology." Bulletin of Mathematical Biology 71, no. 4 (December 20, 2008): 952–79. http://dx.doi.org/10.1007/s11538-008-9389-z.

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3

Alles, Guilherme Rezende, João L. D. Comba, Jean-Marc Vincent, Shin Nagai, and Lucas Mello Schnorr. "Measuring phenology uncertainty with large scale image processing." Ecological Informatics 59 (September 2020): 101109. http://dx.doi.org/10.1016/j.ecoinf.2020.101109.

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4

Yurk, Brian P., and James A. Powell. "Modeling the Effects of Developmental Variation on Insect Phenology." Bulletin of Mathematical Biology 72, no. 6 (January 28, 2010): 1334–60. http://dx.doi.org/10.1007/s11538-009-9494-7.

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5

Senay, Gabriel. "Modeling Landscape Evapotranspiration by Integrating Land Surface Phenology and a Water Balance Algorithm." Algorithms 1, no. 2 (October 30, 2008): 52–68. http://dx.doi.org/10.3390/a1020052.

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6

Yamamura, Norio, Noboru Fujita, Motoyuki Hayashi, Yusuke Nakamura, and Atsushi Yamauchi. "Optimal phenology of annual plants under grazing pressure." Journal of Theoretical Biology 246, no. 3 (June 2007): 530–37. http://dx.doi.org/10.1016/j.jtbi.2007.01.010.

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7

Scharf, Henry R., Mevin B. Hooten, Ryan R. Wilson, George M. Durner, and Todd C. Atwood. "Accounting for phenology in the analysis of animal movement." Biometrics 75, no. 3 (June 24, 2019): 810–20. http://dx.doi.org/10.1111/biom.13052.

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8

Wei, Wei, Wenbin Wu, Zhengguo Li, Peng Yang, and Qingbo Zhou. "Selecting the Optimal NDVI Time-Series Reconstruction Technique for Crop Phenology Detection." Intelligent Automation & Soft Computing 22, no. 2 (November 16, 2015): 237–47. http://dx.doi.org/10.1080/10798587.2015.1095482.

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9

Matechou, Eleni, Stephen N. Freeman, and Richard Comont. "Caste-Specific Demography and Phenology in Bumblebees: Modelling BeeWalk Data." Journal of Agricultural, Biological and Environmental Statistics 23, no. 4 (August 9, 2018): 427–45. http://dx.doi.org/10.1007/s13253-018-0332-y.

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10

Mariano, Greice C., Vanessa G. Staggemeier, Leonor Patricia Cerdeira Morellato, and Ricardo da S. Torres. "Multivariate cyclical data visualization using radial visual rhythms: A case study in phenology analysis." Ecological Informatics 46 (July 2018): 19–35. http://dx.doi.org/10.1016/j.ecoinf.2018.05.003.

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Xu, Qingyun, Guijun Yang, Huiling Long, and Chongchang Wang. "Crop Discrimination in Shandong Province Based on Phenology Analysis of Multi-year Time Series." Intelligent Automation & Soft Computing 19, no. 4 (December 2013): 513–23. http://dx.doi.org/10.1080/10798587.2013.869109.

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12

Almeida, Jurandy, Jefersson A. dos Santos, Bruna Alberton, Ricardo da S. Torres, and Leonor Patricia C. Morellato. "Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees." Ecological Informatics 23 (September 2014): 49–61. http://dx.doi.org/10.1016/j.ecoinf.2013.06.011.

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13

Araya, Sofanit, Bertram Ostendorf, Gregory Lyle, and Megan Lewis. "CropPhenology: An R package for extracting crop phenology from time series remotely sensed vegetation index imagery." Ecological Informatics 46 (July 2018): 45–56. http://dx.doi.org/10.1016/j.ecoinf.2018.05.006.

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14

Stedinger, Jery R., Christine A. Shoemaker, and Robert F. Tenga. "A Stochastic Model of Insect Phenology for a Population with Spatially Variable Development Rates." Biometrics 41, no. 3 (September 1985): 691. http://dx.doi.org/10.2307/2531289.

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15

Lewis-Beck, Colin, Zhengyuan Zhu, Victoria Walker, and Brian Hornbuckle. "Modeling Crop Phenology in the US Corn Belt Using Spatially Referenced SMOS Satellite Data." Journal of Agricultural, Biological and Environmental Statistics 25, no. 4 (October 14, 2020): 657–75. http://dx.doi.org/10.1007/s13253-020-00419-x.

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16

Liu, Zunchi, Kai Liu, Jingjing Zhang, Chuang Yan, T. Ryan Lock, Robert L. Kallenbach, and Zhiyou Yuan. "Fractional coverage rather than green chromatic coordinate is a robust indicator to track grassland phenology using smartphone photography." Ecological Informatics 68 (May 2022): 101544. http://dx.doi.org/10.1016/j.ecoinf.2021.101544.

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17

Laskin, David N., and Gregory J. McDermid. "Evaluating the level of agreement between human and time-lapse camera observations of understory plant phenology at multiple scales." Ecological Informatics 33 (May 2016): 1–9. http://dx.doi.org/10.1016/j.ecoinf.2016.02.005.

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18

Boechel, Tiago, Lucas Micol Policarpo, Gabriel de Oliveira Ramos, Rodrigo da Rosa Righi, and Dhananjay Singh. "Prediction of Harvest Time of Apple Trees: An RNN-Based Approach." Algorithms 15, no. 3 (March 18, 2022): 95. http://dx.doi.org/10.3390/a15030095.

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In the field of agricultural research, Machine Learning (ML) has been used to increase agricultural productivity and minimize its environmental impact, proving to be an essential technique to support decision making. Accurate harvest time prediction is a challenge for fruit production in a sustainable manner, which could eventually reduce food waste. Linear models have been used to estimate period duration; however, they present variability when used to estimate the chronological time of apple tree stages. This study proposes the PredHarv model, which is a machine learning model that uses Recurrent Neural Networks (RNN) to predict the start date of the apple harvest, given the weather conditions related to the temperature expected for the period. Predictions are made from the phenological phase of the beginning of flowering, using a multivariate approach, based on the time series of phenology and meteorological data. The computational model contributes to anticipating information about the harvest date, enabling the grower to better plan activities, avoiding costs, and consequently improving productivity. We developed a prototype of the model and performed experiments with real datasets from agricultural institutions. We evaluated the metrics, and the results obtained in evaluation scenarios demonstrate that the model is efficient, has good generalizability, and is capable of improving the accuracy of the prediction results.

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Zhou, Lei, Hong-lin He, Xiao-min Sun, Li Zhang, Gui-rui Yu, Xiao-li Ren, Jia-yin Wang, and Feng-hua Zhao. "Modeling winter wheat phenology and carbon dioxide fluxes at the ecosystem scale based on digital photography and eddy covariance data." Ecological Informatics 18 (November 2013): 69–78. http://dx.doi.org/10.1016/j.ecoinf.2013.05.003.

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20

Haselhorst, Derek S., David K. Tcheng, J. Enrique Moreno, and Surangi W. Punyasena. "The effects of seasonal and long-term climatic variability on Neotropical flowering phenology: An ecoinformatic analysis of aerial pollen data." Ecological Informatics 41 (September 2017): 54–63. http://dx.doi.org/10.1016/j.ecoinf.2017.06.005.

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Granados, Joel A., Eric A. Graham, Philippe Bonnet, Eric M. Yuen, and Michael Hamilton. "EcoIP: An open source image analysis toolkit to identify different stages of plant phenology for multiple species with pan–tilt–zoom cameras." Ecological Informatics 15 (May 2013): 58–65. http://dx.doi.org/10.1016/j.ecoinf.2013.03.002.

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22

Sakai, Satoki. "Optimal Leaf Phenology and Photosynthetic Capacity of Herbs Dependent on the Genet Density of Herbs in Their Habitat." Journal of Theoretical Biology 166, no. 3 (February 1994): 237–44. http://dx.doi.org/10.1006/jtbi.1994.1021.

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Ide, Reiko, and Hiroyuki Oguma. "A cost-effective monitoring method using digital time-lapse cameras for detecting temporal and spatial variations of snowmelt and vegetation phenology in alpine ecosystems." Ecological Informatics 16 (July 2013): 25–34. http://dx.doi.org/10.1016/j.ecoinf.2013.04.003.

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24

Crawley, Michael J. "New phenology. Elements of mathematical forecasting in ecology." Crop Protection 5, no. 1 (February 1986): 77–78. http://dx.doi.org/10.1016/0261-2194(86)90041-4.

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Inoue, Tomoharu, Shin Nagai, Taku M. Saitoh, Hiroyuki Muraoka, Kenlo N. Nasahara, and Hiroshi Koizumi. "Detection of the different characteristics of year-to-year variation in foliage phenology among deciduous broad-leaved tree species by using daily continuous canopy surface images." Ecological Informatics 22 (July 2014): 58–68. http://dx.doi.org/10.1016/j.ecoinf.2014.05.009.

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26

Siokhin, V. D., A. B. Annenkov, V. V. Osadchyi, and A. P. Horlova. "Capabilities of the WEBBIRDS system in the process of assessing the impact of wind farms on seasonal bird complexes on the example of spring migrants at the Botiieve wind farm in 2013-2021." IOP Conference Series: Earth and Environmental Science 1049, no. 1 (June 1, 2022): 012058. http://dx.doi.org/10.1088/1755-1315/1049/1/012058.

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Abstract This paper contains the analyzed results of field observations of the spring migration of birds on the territory of the Botiieve wind farm in 2013-2021. The work was carried out as part of the planned monitoring of the ornithological situation in the area of the Botiieve wind farm (Pryazovskyi district, Zaporizhzhia region) and also covered the Tubal Estuary formed by the confluence of the Velyka and Mala Domuzla and Akchokrak Rivers and in the mouth of the Korsak River. During each trip, up to 70% of the wind farm area was covered. There were given characteristics of the taxonomic composition of the ornithocomplex, flight phenology, height and direction of migration by seasons and months. In the spring period of 2013-2021, 156,910 individuals of 125 species were recorded in the project area. 52,575 individuals of 92 species of these birds (33.5%) were observed directly within the Botiieve wind farm and buffer zones and there were recorded 104,335 individuals of 99 species (66.5%) at the adjacent wetlands - the Botiieve Ponds and the Tubal Estuary. New methods for collecting, storing and processing information, including mapping, server storage and data processing using two web applications, have been proposed. In order to describe in detail the migration processes in the local area, methods of vector mathematics, as well as computer vision algorithms, were used. The result of the analysis was a gradient map of seasonal bird migration concentration, which allows a differentiated approach to assessing the threats to birds from operating wind turbines. The impact of the Botiieve wind farm on birds during the period of seasonal migrations is estimated to be low.

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Nagai, Shin, Taku M. Saitoh, Nam Jin Noh, Tae Kyung Yoon, Hideki Kobayashi, Rikie Suzuki, Kenlo Nishida Nasahara, Yowhan Son, and Hiroyuki Muraoka. "Utility of information in photographs taken upwards from the floor of closed-canopy deciduous broadleaved and closed-canopy evergreen coniferous forests for continuous observation of canopy phenology." Ecological Informatics 18 (November 2013): 10–19. http://dx.doi.org/10.1016/j.ecoinf.2013.05.005.

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28

Lima, R. S. O., E. C. R. Machado, A. P. P. Silva, B. S. Marques, M. F. Gonçalves, and S. J. P. Carvalho. "Growth and Development of Purple Nutsedge Based on Days or Thermal Units." Planta Daninha 33, no. 2 (June 2015): 165–73. http://dx.doi.org/10.1590/0100-83582015000200001.

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This work was carried out with the objective of elaborating mathematical models to predict growth and development of purple nutsedge (Cyperus rotundus) based on days or accumulated thermal units (growing degree days). Thus, two independent trials were developed, the first with a decreasing photoperiod (March to July) and the second with an increasing photoperiod (August to November). In each trial, ten assessments of plant growth and development were performed, quantifying total dry matter and the species phenology. After that, phenology was fit to first degree equations, considering individual trials or their grouping. In the same way, the total dry matter was fit to logistic-type models. In all regressions four temporal scales possibilities were assessed for the x axis: accumulated days or growing degree days (GDD) with base temperatures (Tb) of 10, 12 and 15 oC. For both photoperiod conditions, growth and development of purple nutsedge were adequately fit to prediction mathematical models based on accumulated thermal units, highlighting Tb = 12 oC. Considering GDD calculated with Tb = 12 oC, purple nutsedge phenology may be predicted by y = 0.113x, while species growth may be predicted by y = 37.678/(1+(x/509.353)-7.047).

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29

Ford, E. David, Anne Avery, and R. Ford. "Simulation of branch growth in the Pinaceae: Interactions of morphology, phenology, foliage productivity, and the requirement for structural support, on the export of carbon." Journal of Theoretical Biology 146, no. 1 (September 1990): 15–36. http://dx.doi.org/10.1016/s0022-5193(05)80042-6.

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30

Swartzman, Gordon. "Phenological Forecasting New Phenology: Elements of Mathematical Forecasting in Ecology A. S. Podolsky." BioScience 35, no. 8 (September 1985): 514. http://dx.doi.org/10.2307/1309825.

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31

Kristensen, Nadiah Pardede, Jacob Johansson, Jörgen Ripa, and Niclas Jonzén. "Phenology of two interdependent traits in migratory birds in response to climate change." Proceedings of the Royal Society B: Biological Sciences 282, no. 1807 (May 22, 2015): 20150288. http://dx.doi.org/10.1098/rspb.2015.0288.

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In migratory birds, arrival date and hatching date are two key phenological markers that have responded to global warming. A body of knowledge exists relating these traits to evolutionary pressures. In this study, we formalize this knowledge into general mathematical assumptions, and use them in an ecoevolutionary model. In contrast to previous models, this study novelty accounts for both traits—arrival date and hatching date—and the interdependence between them, revealing when one, the other or both will respond to climate. For all models sharing the assumptions, the following phenological responses will occur. First, if the nestling-prey peak is late enough, hatching is synchronous with, and arrival date evolves independently of, prey phenology. Second, when resource availability constrains the length of the pre-laying period, hatching is adaptively asynchronous with prey phenology. Predictions for both traits compare well with empirical observations. In response to advancing prey phenology, arrival date may advance, remain unchanged, or even become delayed; the latter occurring when egg-laying resources are only available relatively late in the season. The model shows that asynchronous hatching and unresponsive arrival date are not sufficient evidence that phenological adaptation is constrained. The work provides a framework for exploring microevolution of interdependent phenological traits.

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Moorman, Gary W., James L. Rosenberger, and Leslie A. Gladstone. "Comparison of two air temperature based models for predicting phenophase occurrence in Persian lilacs (Syringa chinensis) cultivar Red Rothomagensis." Canadian Journal of Botany 68, no. 5 (May 1, 1990): 1113–16. http://dx.doi.org/10.1139/b90-140.

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Phenological data collected for 9–11 years from genetically uniform Persian lilacs (Syringa chinensis L.) cultivar Red Rothomagensis were analyzed to determine whether the number of days elapsed between vegetative bud break and flower bud break is correlated with either the accumulated growing degree-days or the average daily temperature. The lack of statistically significant correlations between these variables suggests these mathematical models cannot accurately predict the onset of flower bud break for this cultivar. Key words: degree-days, growth model, phenology.

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Lerin, Sabrina, Daniel Santos Grohs, Marcus André Kurtz Almança, Marcos Botton, Paulo Mello-Farias, and José Carlos Fachinello. "Prediction model for phenology of grapevine cultivars with hot water treatment." Pesquisa Agropecuária Brasileira 52, no. 10 (October 2017): 887–95. http://dx.doi.org/10.1590/s0100-204x2017001000008.

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Abstract: The objective of this work was to prepare a prediction model for the phenology of grapevine cultivars (Bordô, Cabernet Sauvignon, Moscato Embrapa, Paulsen 1103, SO4, and IAC 572) using hot water treatment. The heat treatment with hot water consisted of combinations of three temperatures (50, 53, and 55°C) and three time periods (30, 45, and 60 min), with or without previous hydration for 30 min. After the treatments, the cuttings were planted in the field and their phenological development was evaluated during two months. The six studied cultivars presented different responses to the effects of the factors temperature and time, but did not differ significantly regarding hydration. It was possible to develop a mathematical model for the use of hot water treatment in grapevine cuttings, based on phenological development ( y phenology = 48.268 − 0.811 x 1 − 0.058 x 2) and validated by the variables sprouting and root emission. From the developed model, it is recommended that the hot water treatment be applied in the temperature range between 48 and 51°C for cuttings of all cultivars.

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Silva, A. P. P., B. S. Marques, R. S. O. Lima, E. C. R. Machado, M. F. Gonçalves, and S. J. P. Carvalho. "Growth and development of honey weed based on days or thermal units." Planta Daninha 32, no. 1 (March 2014): 81–89. http://dx.doi.org/10.1590/s0100-83582014000100009.

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This work was carried out with the objective of evaluating the growth and development of honey weed (Leonurus sibiricus) based on days or thermal units (growing degree days). Thus, two independent trials were developed to quantify the phenological development and total dry mass accumulation in increasing or decreasing photoperiod conditions. Considering only one growing season, honey weed phenological development was perfectly fit to day scale or growing degree days, but with no equivalence between seasons, with the plants developing faster at increasing photoperiods, and flowering 100 days after seeding. Even day-time scale or thermal units were not able to estimate general honey weed phenology during the different seasons of the year. In any growing condition, honey weed plants were able to accumulate a total dry mass of over 50 g per plant. Dry mass accumulation was adequately fit to the growing degree days, with highlights to a base temperature of 10 ºC. Therefore, a higher environmental influence on species phenology and a lower environmental influence on growth (dry mass) were observed, showing thereby that other variables, such as the photoperiod, may potentially complement the mathematical models.

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Zuo, Lu, Ronggao Liu, Yang Liu, and Rong Shang. "Effect of Mathematical Expression of Vegetation Indices on the Estimation of Phenology Trends from Satellite Data." Chinese Geographical Science 29, no. 5 (October 2019): 756–67. http://dx.doi.org/10.1007/s11769-019-1070-y.

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Ma, Xin Ping, Hong Ying Bai, and Ying Na He. "The Effects of Climate Warming on Mainly Plant Phenological Phase of North Qinling Mountains - In Xi 'an as an Example." Advanced Materials Research 962-965 (June 2014): 1353–62. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.1353.

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To reveal the influence of the temperature changes on plant phenology,The phenological data and temperature log data nearly 50 years in Xi 'an station was used to research the relationship between the temperature and phenological responses, which combined with mathematical statistics methods such as Julian day、correlation coefficient , the results showed: (1) 1962-2009, The temperature of beginning phenological period、phenological late and growth period are on the rise, of which the temperature of beginning phase increased most obviously; (2) The M - K mutation test the mutation in Xi ‘an annual average temperature of the year is 1995, the beginning and end of phenological mutation and the growth period is: 1998, 1997 and 2003; (3) Xi 'an plant phenology beginning period in advance, growth period prolonged, the end period delayed, there is an upward trend in temperatures in different historical periods; (4) The temperature mutations showed the trend of ahead, the beginning phenological period before and after the end of phenological showed a trend of later, after the end of the degree to which were greater than the extent of the beginning period in advance, and after the mutation of phenological change trend mutations were greater than before, that means the temperature rising added plant phenological period of delay, and extend the ahead of time.

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Ruml, Mirjana, and Todor Vulic. "Importance of phenological observations and predictions in agriculture." Journal of Agricultural Sciences, Belgrade 50, no. 2 (2005): 217–25. http://dx.doi.org/10.2298/jas0502217r.

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Phenology can contribute to many scientific disciplines from climate change, biodiversity, agriculture and forestry to human health. The knowledge of timing of phenological events and their variability can provide valuable data for planning, organizing and timely execution of certain standard and special (preventive and protective) agricultural activities that require advanced information on the dates of specific stages of crop development. Mathematical models are the basic tools to predict the timing of phenological events. There are two types of phenological models: physiologically-based and statistical. Most of the existing models are statistical and serve to predict the onset of different phenophases according to the air temperature. These models are site- and species-specific and cannot be applied to a wide range of species and climatic conditions.

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Roman, Erivelton S., A. Gordon Thomas, Stephen D. Murphy, and Clarence J. Swanton. "Modeling germination and seedling elongation of common lambsquarters (Chenopodium album)." Weed Science 47, no. 2 (April 1999): 149–55. http://dx.doi.org/10.1017/s0043174500091554.

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The ability to predict time of weed seedling emergence relative to the crop is an important component of a mechanistic model describing weed and crop competition. In this paper, we hypothesized that the process of germination could be described by the interaction of temperature and water potential and that the rate of seedling shoot and radicle elongation vary as a function of temperature. To test these hypotheses, incubator studies were conducted using seeds and seedlings of common lambsquarters. Probit analysis was used to account for variation in cardinal temperatures and base water potentials and to develop parameters for a new mathematical model that describes seed germination and shoot and radicle elongation in terms of hydrothermal time and temperature, respectively. This hydrothermal time model describes the phenology of seed germination using a single curve, generated from the relationship of temperature and water potential.

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Liu, Cao, Shen, Chen, Wang, and Zhang. "How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?" Remote Sensing 11, no. 18 (September 13, 2019): 2137. http://dx.doi.org/10.3390/rs11182137.

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As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.

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De Lemos, Hugo, Michel M. Verstraete, and Mary Scholes. "Parametric Models to Characterize the Phenology of the Lowveld Savanna at Skukuza, South Africa." Remote Sensing 12, no. 23 (November 30, 2020): 3927. http://dx.doi.org/10.3390/rs12233927.

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Mathematical models, such as the logistic curve, have been extensively used to model the temporal evolution of biological processes, though other similarly shaped functions could be (and sometimes have been) used for this purpose. Most previous studies focused on agricultural regions in the Northern Hemisphere and were based on the Normalized Difference Vegetation Index (NDVI). This paper compares the capacity of four parametric double S-shaped models (Gaussian, Hyperbolic Tangent, Logistic, and Sine) to represent the seasonal phenology of an unmanaged, protected savanna biome in South Africa’s Lowveld, using the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) generated by the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) processing system on the basis of data originally collected by National Aeronautics and Space Administration (NASA)’s Multi-angle Imaging SpectroRadiometer (MISR) instrument since 24 February 2000. FAPAR time series are automatically split into successive vegetative seasons, and the models are inverted against those irregularly spaced data to provide a description of the seasonal fluctuations despite the presence of noise and missing values. The performance of these models is assessed by quantifying their ability to account for the variability of remote sensing data and to evaluate the Gross Primary Productivity (GPP) of vegetation, as well as by evaluating their numerical efficiency. Simulated results retrieved from remote sensing are compared to GPP estimates derived from field measurements acquired at Skukuza’s flux tower in the Kruger National Park, which has also been operational since 2000. Preliminary results indicate that (1) all four models considered can be adjusted to fit an FAPAR time series when the temporal distribution of the data is sufficiently dense in both the growing and the senescence phases of the vegetative season, (2) the Gaussian and especially the Sine models are more sensitive than the Hyperbolic Tangent and Logistic to the temporal distribution of FAPAR values during the vegetative season, and, in particular, to the presence of long temporal gaps in the observational data, and (3) the performance of these models to simulate the phenology of plants is generally quite sensitive to the presence of unexpectedly low FAPAR values during the peak period of activity and to the presence of long gaps in the observational data. Consequently, efforts to screen out outliers and to minimize those gaps, especially during the rainy season (vegetation’s growth phase), would go a long way to improve the capacity of the models to adequately account for the evolution of the canopy cover and to better assess the relation between FAPAR and GPP.

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Hjelkrem, Anne-Grete Roer, Andrea Ficke, Unni Abrahamsen, Ingerd Skow Hofgaard, and Guro Brodal. "Prediction of leaf Bloch disease risk in Norwegian spring wheat based on weather factors and host phenology." European Journal of Plant Pathology 160, no. 1 (February 17, 2021): 199–213. http://dx.doi.org/10.1007/s10658-021-02235-6.

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AbstractLeaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.

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Machado, E. C. R., R. S. O. Lima, A. P. P. Silva, B. S. Marques, M. F. Gonçalves, and S. J. P. Carvalho. "Initial growth and development of southern sandbur based on thermal units." Planta Daninha 32, no. 2 (June 2014): 335–43. http://dx.doi.org/10.1590/s0100-83582014000200011.

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Availability of basic information on weed biology is an essential tool for designing integrated management programs for agricultural systems. Thus, this study was carried out in order to calculate the base temperature (Tb) of southern sandbur (Cenchrus echinatus), as well as fit the initial growth and development of the species to accumulated thermal units (growing degree days - GDD). For that purpose, experimental populations were sown six times in summer/autumn conditions (decreasing photoperiod) and six times in winter/spring condition (increasing photoperiod). Southern sandbur phenological evaluations were carried out, on alternate days, and total dry matter was measured when plants reached the flowering stage. All the growth and development fits were performed based on thermal units by assessing five base temperatures, as well as the absence of it. Southern sandbur development was best fit with Tb = 12 ºC, with equation y = 0,0993x, where y is the scale of phenological stage and x is the GDD. On average, flowering was reached at 518 GDD. Southern sandbur phenology may be predicted by using mathematical models based on accumulated thermal units, adopting Tb = 12 ºC. However, other environmental variables may also interfere with species development, particularly photoperiod.

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Woods, John O., Ulf Singh-Blom, Jon M. Laurent, Kriston L. McGary, and Edward M. Marcotte. "Prediction of gene–phenotype associations in humans, mice, and plants using phenologs." BMC Bioinformatics 14, no. 1 (2013): 203. http://dx.doi.org/10.1186/1471-2105-14-203.

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Lykhovyd, P. V. "Seasonal dynamics of normalized difference vegetation index in some winter and spring crops in the South of Ukraine." Agrology 4, no. 4 (2021): 187–93. http://dx.doi.org/10.32819/021022.

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Spatial crop monitoring using vegetation indices is one of the most promising technologies for crop mapping and remote phenological observations. The aim of the study was to determine the patterns of seasonal dynamics of the spatial normalized difference vegetation index for the main crops grown in the south of Ukraine and to connect it to their phenology. Remote sensing data provided by the OneSoil AI platform, which uses Sentinel-1 and Sentinel-2 imagery as a basis, was used to derive the monthly index values for the 2016–2021 growing season for nine selected crops grown in the experimental fields at the NAAS Institute of Irrigated Agriculture, Kherson, Ukraine. The fallow field was also included in the study to determine the cutoff values of the vegetation index, which are not representative of any healthy vegetation. It was determined that each crop has its unique pattern of the dynamics of the vegetation index, except for winter wheat and winter barley, which demonstrated quite similar models. The peak values of the vegetation index were observed in May for winter crops (wheat, barley, rapeseed) and early-spring crops (chickpea, peas), while the late-spring crops (grain corn, grain sorghum, soybeans, sunflower) reached the peak values in July. It is possible to suggest that the highest demand for mineral nutrition and watering will fall in the mentioned time periods of late spring and midsummer. Phenological monitoring revealed that the highest values of the spatial normalized difference vegetation index were observed in the following stages of crop growth, namely: winter wheat, winter barley – stem elongation; winter rapeseed – flowering; chickpea – branching; peas – budding and flowering; sunflower – stem growth; soybeans - pod formation; grain sorghum – panicle ejection and flowering; grain corn – panicle ejection and flowering. The results provide novel information for further implementation in the mathematical models for automation of crops recognition, mapping, and phenological observations based on the remote sensing data. Further scientific research in this direction will be aimed at increasing the spectrum of crops studied and a detailed investigation of the relationship between the value of the normalized difference vegetation index and their phenology.

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Shim, Junghyun, Nonoy B. Bandillo, and Rosalyn B. Angeles-Shim. "Finding Needles in a Haystack: Using Geo-References to Enhance the Selection and Utilization of Landraces in Breeding for Climate-Resilient Cultivars of Upland Cotton (Gossypium hirsutum L.)." Plants 10, no. 7 (June 26, 2021): 1300. http://dx.doi.org/10.3390/plants10071300.

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The genetic uniformity of cultivated cotton as a consequence of domestication and modern breeding makes it extremely vulnerable to abiotic challenges brought about by major climate shifts. To sustain productivity amidst worsening agro-environments, future breeding objectives need to seriously consider introducing new genetic variation from diverse resources into the current germplasm base of cotton. Landraces are genetically heterogeneous, population complexes that have been primarily selected for their adaptability to specific localized or regional environments. This makes them an invaluable genetic resource of novel allelic diversity that can be exploited to enhance the resilience of crops to marginal environments. The utilization of cotton landraces in breeding programs are constrained by the phenology of the plant and the lack of phenotypic information that can facilitate efficient selection of potential donor parents for breeding. In this review, the genetic value of cotton landraces and the major challenges in their utilization in breeding are discussed. Two strategies namely Focused Identification of Germplasm Strategy and Environmental Association Analysis that have been developed to effectively screen large germplasm collections for accessions with adaptive traits using geo-reference-based, mathematical modelling are highlighted. The potential applications of both approaches in mining available cotton landrace collections are also presented.

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Haughton, A. J., G. T. Champion, C. Hawes, M. S. Heard, D. R. Brooks, D. A. Bohan, S. J. Clark, et al. "Invertebrate responses to the management of genetically modified herbicide–tolerant and conventional spring crops. II. Within-field epigeal and aerial arthropods." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 358, no. 1439 (October 16, 2003): 1863–77. http://dx.doi.org/10.1098/rstb.2003.1408.

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The effects of the management of genetically modified herbicide–tolerant (GMHT) crops on the abundances of aerial and epigeal arthropods were assessed in 66 beet, 68 maize and 67 spring oilseed rape sites as part of the Farm Scale Evaluations of GMHT crops. Most higher taxa were insensitive to differences between GMHT and conventional weed management, but significant effects were found on the abundance of at least one group within each taxon studied. Numbers of butterflies in beet and spring oilseed rape and of Heteroptera and bees in beet were smaller under the relevant GMHT crop management, whereas the abundance of Collembola was consistently greater in all GMHT crops. Generally, these effects were specific to each crop type, reflected the phenology and ecology of the arthropod taxa, were indirect and related to herbicide management. These results apply generally to agriculture across Britain, and could be used in mathematical models to predict the possible long–term effects of the widespread adoption of GMHT technology. The results for bees and butterflies relate to foraging preferences and might or might not translate into effects on population densities, depending on whether adoption leads to forage reductions over large areas. These species, and the detritivore Collembola, may be useful indicator species for future studies of GMHT management.

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San Bautista, Alberto, David Fita, Belén Franch, Sergio Castiñeira-Ibáñez, Patricia Arizo, María José Sánchez-Torres, Inbal Becker-Reshef, Antonio Uris, and Constanza Rubio. "Crop Monitoring Strategy Based on Remote Sensing Data (Sentinel-2 and Planet), Study Case in a Rice Field after Applying Glycinebetaine." Agronomy 12, no. 3 (March 15, 2022): 708. http://dx.doi.org/10.3390/agronomy12030708.

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World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management.

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Kraft, Nathan J. B., Oscar Godoy, and Jonathan M. Levine. "Plant functional traits and the multidimensional nature of species coexistence." Proceedings of the National Academy of Sciences 112, no. 3 (January 5, 2015): 797–802. http://dx.doi.org/10.1073/pnas.1413650112.

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Understanding the processes maintaining species diversity is a central problem in ecology, with implications for the conservation and management of ecosystems. Although biologists often assume that trait differences between competitors promote diversity, empirical evidence connecting functional traits to the niche differences that stabilize species coexistence is rare. Obtaining such evidence is critical because traits also underlie the average fitness differences driving competitive exclusion, and this complicates efforts to infer community dynamics from phenotypic patterns. We coupled field-parameterized mathematical models of competition between 102 pairs of annual plants with detailed sampling of leaf, seed, root, and whole-plant functional traits to relate phenotypic differences to stabilizing niche and average fitness differences. Single functional traits were often well correlated with average fitness differences between species, indicating that competitive dominance was associated with late phenology, deep rooting, and several other traits. In contrast, single functional traits were poorly correlated with the stabilizing niche differences that promote coexistence. Niche differences could only be described by combinations of traits, corresponding to differentiation between species in multiple ecological dimensions. In addition, several traits were associated with both fitness differences and stabilizing niche differences. These complex relationships between phenotypic differences and the dynamics of competing species argue against the simple use of single functional traits to infer community assembly processes but lay the groundwork for a theoretically justified trait-based community ecology.

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Zhao, Rongkun, Yuechen Li, and Mingguo Ma. "Mapping Paddy Rice with Satellite Remote Sensing: A Review." Sustainability 13, no. 2 (January 7, 2021): 503. http://dx.doi.org/10.3390/su13020503.

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Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.

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Zhao, Rongkun, Yuechen Li, and Mingguo Ma. "Mapping Paddy Rice with Satellite Remote Sensing: A Review." Sustainability 13, no. 2 (January 7, 2021): 503. http://dx.doi.org/10.3390/su13020503.

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Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.

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