Search results for: photosynthesis

Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: M. Szechynska-Hebda, M. Sobczak, E. Rozanska, J. Troczynska, Z. Banaszak, N. Hordyńska, M. Dyda, M. Wedzony

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Chloroplasts, as essential organelles for photosynthesis, play a critical role in plant development. However, disturbances in the proper functioning of chloroplasts, in the extreme case manifesting as albinism of tissues and whole plants, are a phenomenon often occurring in conditions deviating from natural (e.g., in vitro cultures applied in breeding programs). Using whole-transcriptome analysis (RNA-Seq) together with light, fluorescent and electron microscopy, it was shown, that development of chloroplasts and formation of green or albino plants in the androgenesis process are genotype-dependent; however, they could be modulated by sub-optimal temperature treatment. The reprogramming of the microspore development from gametophytic to sporophytic, and then regeneration of green plant can be positively regulated by cold stress (4 ⁰C). A high temperature stress (32 ⁰C) can induce androgenesis, but it is a factor negatively influencing green plant regeneration (promoting albinism). A similar effect on microspores, androgenesis, and subsequent chloroplast formation, is elicited as a result of postponing the date of spike collection from spring to summer in field conditions (natural temperature rise). It is determined in both environmental or genotypic manner. The delay of the sowing date (environmental effect) or growing of late genotypes (genotypic effect) result in spike maturation at higher temperatures and significantly enhance albino plant formation in androgenesis process. Such a temperature system (4 ⁰C vs. 32 ⁰C) was used to study the chloroplast biogenesis process in wheat and triticale. It was shown, that efficiency of physiological processes differentiates microspore development during cold reprograming in genotypes susceptible and resistant to androgenesis. Moreover, a great variation in developmental stages of the microspores in one anther is observed for susceptible genotypes. Microspores that are more physiologically active under cold conditions can activate signaling pathways and processes, which provide an appropriate supply of metabolites to cell compartments. This, in turn, fully correlates with the genotype-dependent efficiency of chloroplast formation (or different types of plastid) at particular steps of androgenesis. The effect obtained after applying a high temperature stress is different. High temperature causes a significant acceleration of microspore development and less variation in developmental stages at the end of the treatment. Therefore, the developmental diversity of the microspores in one anther seems to be a critical factor for subsequent cell and chloroplast differentiation. The work was financed by Ministry of Agriculture and Rural Development within Program: 'Biological Progress in Plant Production', project no HOR.hn.802.15.2018

Keywords: androgenesis, chloroplast biogenesis, temperature stress, wheat

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Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

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Authors: Tatsuya Kasuga, Hidehisa Shimada, Kimio Oguchi

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Recent rapid progress in ICT (Information and Communication Technology) has advanced the penetration of sensor networks (SNs) and their attractive applications. Agriculture is one of the fields well able to benefit from ICT. Plant factories control several parameters related to plant growth in closed areas such as air temperature, humidity, water, culture medium concentration, and artificial lighting by using computers and AI (Artificial Intelligence) is being researched in order to obtain stable and safe production of vegetables and medicinal plants all year anywhere, and attain self-sufficiency in food. By providing isolation from the natural environment, a plant factory can achieve higher productivity and safe products. However, the biggest issue with plant factories is the return on investment. Profits are tenuous because of the large initial investments and running costs, i.e. electric power, incurred. At present, LED (Light Emitting Diode) lights are being adopted because they are more energy-efficient and encourage photosynthesis better than the fluorescent lamps used in the past. However, further cost reduction is essential. This paper introduces experiments that reveal which color of LED lighting best enhances the growth of cultured radish sprouts. Radish sprouts were cultivated in the experimental environment formed by a hydroponics kit with three cultivation shelves (28 samples per shelf) each with an artificial lighting rack. Seven LED arrays of different color (white, blue, yellow green, green, yellow, orange, and red) were compared with a fluorescent lamp as the control. Lighting duration was set to 12 hours a day. Normal water with no fertilizer was circulated. Seven days after germination, the length, weight and area of leaf of each sample were measured. Electrical power consumption for all lighting arrangements was also measured. Results and discussions: As to average sample length, no clear difference was observed in terms of color. As regards weight, orange LED was less effective and the difference was significant (p < 0.05). As to leaf area, blue, yellow and orange LEDs were significantly less effective. However, all LEDs offered higher productivity per W consumed than the fluorescent lamp. Of the LEDs, the blue LED array attained the best results in terms of length, weight and area of leaf per W consumed. Conclusion and future works: An experiment on radish sprout cultivation under 7 different color LED arrays showed no clear difference in terms of sample size. However, if electrical power consumption is considered, LEDs offered about twice the growth rate of the fluorescent lamp. Among them, blue LEDs showed the best performance. Further cost reduction e.g. low power lighting remains a big issue for actual system deployment. An automatic plant monitoring system with sensors is another study target.

Keywords: electric power consumption, LED color, LED lighting, plant factory

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Authors: Mogale T. E., Ayisi K. K., Munjonji L., Kifle Y. G.

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Grain sorghum and cowpea are important staple crops in many areas of South Africa, particularly the Limpopo Province. The two crops are produced under a wide range of unsustainable conventional methods, which reduces productivity in the long run. Climate-smart traditional methods such as intercropping can be adopted to ensure sustainable production of these important two crops in the province. A no-tillage field experiment was laid out in a randomised complete block design (RCBD) with four replications over two seasons in two distinct agro-ecological zones, Syferkuil and Ofcolacoin, the province to assess the productivity of sorghum-cowpea intercropped under two cowpea densities.LCi Ultra compact photosynthesis machine was used to collect photosynthetic rate data biweekly between 11h00 and 13h00 until physiological maturity. Biomass and grain yield of the component crops in binary and sole cultures were determined at harvest maturity from middle rows of 2.7 m2 area. The biomass was oven dried in the laboratory at 65oC till constant weight. To obtain grain yield, harvested sorghum heads and cowpea pods were threshed, cleaned, and weighed. Harvest index (HI) and land equivalent ratio (LER) of the two crops were calculated to assess intercrop productivity relative to sole cultures. Data was analysed using the statistical analysis software system (SAS) 9.4 version, followed by mean separation using the least significant difference method. The photosyntheticrate of sorghum-cowpea intercrop was influenced by cowpea density and sorghum cultivar. Photosynthetic rate under low density was higher compared to high density, but this was dependent on the growing conditions. Dry biomass accumulation, grain yield, and harvest index differed among the sorghum cultivars and cowpea in both binary and sole cultures at the two test locations during the 2018/19 and 2020/21 growing seasons. Cowpea grain and dry biomass yields werein excess of 60% under high density compared to low density in both binary and sole cultures. The results revealed that grain yield accumulation of sorghum cultivars was influenced by the density of the companion cowpea crop as well as the production season. For instant, at Syferkuil, Enforcer and Ns5511 accumulated high yield under low density, whereas, at Ofcolaco, the higher yield was recorded under high density. Generally, under low cowpea density, cultivar Enforcer produced relatively higher grain yield whereas, under higher density, Titan yield was superior. The partial and total LER varied with growing season and the treatments studied. The total LERs exceeded 1.0 at the two locations across seasons, ranging from 1.3 to 1.8. From the results, it can be concluded that resources were used more efficiently in sorghum-cowpea intercrop at both Syferkuil and Ofcolaco. Furthermore, intercropping system improved photosynthetic rate, grain yield, and dry matter accumulation of sorghum and cowpea depending on growing conditions and density of cowpea. Hence, the sorghum-cowpea intercropping system can be adopted as a climate-smart practice for sustainable production in the Limpopo province.

Keywords: cowpea, climate-smart, grain sorghum, intercropping

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Authors: A. K. Tekile, I. L. Kim, J. Y. Lee

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Human activities put freshwater quality under risk, mainly due to expansion of agriculture and industries, damming, diversion and discharge of inadequately treated wastewaters. The rapid human population growth and climate change escalated the problem. External controlling actions on point and non-point pollution sources are long-term solution to manage water quality. To have a holistic approach, these mechanisms should be coupled with the in-water control strategies. The available in-lake or river methods are either costly or they have some adverse effect on the ecological system that the search for an alternative and effective solution with a reasonable balance is still going on. This study aimed at the physical and chemical water quality improvement in a stagnant Yeo-cheon River reach (Korea), which has recently shown sign of water quality problems such as scum formation and fish death. The river water quality was monitored, for the duration of three months by operating only water flow generator in the first two weeks and then ultrasonic irradiation device was coupled to the flow unit for the remaining duration of the experiment. In addition to assessing the water quality improvement, the correlation among the parameters was analyzed to explain the contribution of the ultra-sonication. Generally, the combined strategy showed localized improvement of water quality in terms of dissolved oxygen, Chlorophyll-a and dissolved reactive phosphate. At locations under limited influence of the system operation, chlorophyll-a was highly increased, but within 25 m of operation the low initial value was maintained. The inverse correlation coefficient between dissolved oxygen and chlorophyll-a decreased from 0.51 to 0.37 when ultrasonic irradiation unit was used with the flow, showing that ultrasonic treatment reduced chlorophyll-a concentration and it inhibited photosynthesis. The relationship between dissolved oxygen and reactive phosphate also indicated that influence of ultra-sonication was higher than flow on the reactive phosphate concentration. Even though flow increased turbidity by suspending sediments, ultrasonic waves canceled out the effect due to the agglomeration of suspended particles and the follow-up settling out. There has also been variation of interaction in the water column as the decrease of pH and dissolved oxygen from surface to the bottom played a role in phosphorus release into the water column. The variation of nitrogen and dissolved organic carbon concentrations showed mixed trend probably due to the complex chemical reactions subsequent to the operation. Besides, the intensive rainfall and strong wind around the end of the field trial had apparent impact on the result. The combined effect of water flow and ultrasonic irradiation was a cumulative water quality improvement and it maintained the dissolved oxygen and chlorophyll-a requirement of the river for healthy ecological interaction. However, the overall improvement of water quality is not guaranteed as effectiveness of ultrasonic technology requires long-term monitoring of water quality before, during and after treatment. Even though, the short duration of the study conducted here has limited nutrient pattern realization, the use of ultrasound at field scale to improve water quality is promising.

Keywords: stagnant, ultrasonic irradiation, water flow, water quality

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Authors: Jennifer Buz, Alvin Spivey

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The most common greenhouse gas emitted through human activities, carbon dioxide (CO2), is naturally consumed by plants during photosynthesis. This process is actively being monetized by companies wishing to offset their carbon dioxide emissions. For example, companies are now able to purchase protections for vegetated land due-to-be clear cut or purchase barren land for reforestation. Therefore, by actively preventing the destruction/decay of plant matter or by introducing more plant matter (reforestation), a company can theoretically offset some of their emissions. One of the biggest issues in the carbon credit market is validating and verifying carbon offsets. There is a need for a system that can accurately and frequently ensure that the areas sold for carbon credits have the vegetation mass (and therefore for carbon offset capability) they claim. Traditional techniques for measuring vegetation mass and determining health are costly and require many person-hours. Orbital Sidekick offers an alternative approach that accurately quantifies carbon mass and assesses vegetation health through satellite hyperspectral imagery, a technique which enables us to remotely identify material composition (including plant species) and condition (e.g., health and growth stage). How much carbon a plant is capable of storing ultimately is tied to many factors, including material density (primarily species-dependent), plant size, and health (trees that are actively decaying are not effectively storing carbon). All of these factors are capable of being observed through satellite hyperspectral imagery. This abstract focuses on speciation. To build a species classification model, we matched pixels in our remote sensing imagery to plants on the ground for which we know the species. To accomplish this, we collaborated with the researchers at the Teakettle Experimental Forest. Our remote sensing data comes from our airborne “Kato” sensor, which flew over the study area and acquired hyperspectral imagery (400-2500 nm, 472 bands) at ~0.5 m/pixel resolution. Coverage of the entire teakettle experimental forest required capturing dozens of individual hyperspectral images. In order to combine these images into a mosaic, we accounted for potential variations of atmospheric conditions throughout the data collection. To do this, we ran an open source atmospheric correction routine called ISOFIT1 (Imaging Spectrometer Optiman FITting), which converted all of our remote sensing data from radiance to reflectance. A database of reflectance spectra for each of the tree species within the study area was acquired using the Teakettle stem map and the geo-referenced hyperspectral images. We found that a wide variety of machine learning classifiers were able to identify the species within our images with high (>95%) accuracy. For the most robust quantification of carbon mass and the best assessment of the health of a vegetated area, speciation is critical. Through the use of high resolution hyperspectral data, ground-truth databases, and complex analytical techniques, we are able to determine the species present within a pixel to a high degree of accuracy. These species identifications will feed directly into our carbon mass model.

Keywords: hyperspectral, satellite, carbon, imagery, python, machine learning, speciation

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Authors: Natalia Hordynska, Magdalena Szechynska-Hebda, Miroslaw Sobczak, Elzbieta Rozanska, Joanna Troczynska, Zofia Banaszak, Maria Wedzony

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Albinism is a common problem encountered in wheat and triticale breeding programs, which require in vitro culture steps e.g. generation of doubled haploids via androgenesis process. Genetic factor is a major determinant of albinism, however, environmental conditions such as temperature and media composition influence the frequency of albino plant formation. Cold incubation of wheat and triticale spikes induced a switch from gametophytic to sporophytic development. Further, androgenic structures formed from anthers of the genotypes susceptible to androgenesis or treated with cold stress, had a pool of structurally primitive plastids, with small starch granules or swollen thylakoids. High temperature was a factor inducing andro-genesis of wheat and triticale, but at the same time, it was a factor favoring the formation of albino plants. In genotypes susceptible to albinism or after heat stress conditions, cells formed from anthers were vacuolated, and plastids were eliminated. Partial or complete loss of chlorophyll pigments and incomplete differentiation of chloroplast membranes result in formation of tissues or whole plant unable to perform photosynthesis. Indeed, susceptibility to the andro-genesis process was associated with an increase of total concentration of photosynthetic pigments in anthers, spikes and regenerated plants. The proper balance of the synthesis of various pigments, was the starting point for their proper incorporation into photosynthetic membranes. In contrast, genotypes resistant to the androgenesis process and those treated with heat, contained 100 times lower content of photosynthetic pigments. In particular, the synthesis of violaxanthin, zeaxanthin, lutein and chlorophyll b was limited. Furthermore, deregulation of starch and lipids synthesis, which led to the formation of very complex starch granules and an increased number of oleosomes, respectively, correlated with the reduction of the efficiency of androgenesis. The content of other sugars varied depending on the genotype and the type of stress. The highest content of various sugars was found for genotypes susceptible to andro-genesis, and highly reduced for genotypes resistant to androgenesis. The most important sugars seem to be glucose and fructose. They are involved in sugar sensing and signaling pathways, which affect the expression of various genes and regulate plant development. Sucrose, on the other hand, seems to have minor effect at each stage of the androgenesis. The sugar metabolism was related to metabolic activity of microspores. The genotypes susceptible to androgenesis process had much faster mitochondrium- and chloroplast-dependent energy conversion and higher heat production by tissues. Thus, the effectiveness of metabolic processes, their balance and the flexibility under the stress was a factor determining the direction of microspore development, and in the later stages of the androgenesis process, a factor supporting the induction of androgenic structures, chloroplast formation and the regeneration of green plants. The work was financed by Ministry of Agriculture and Rural Development within Program: ‘Biological Progress in Plant Production’, project no HOR.hn.802.15.2018.

Keywords: androgenesis, chloroplast, metabolism, temperature stress

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