Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Field-transcriptome analyses reveal developmental transitions during flowering in cassava (Manihot esculenta Crantz).

KEY MESSAGE: We characterized genes that function in the photoperiodic flowering pathway in cassava. Transcriptome analysis of field-grown plants revealed characteristic expression patterns of these genes, demonstrating that field-grown cassava experiences two distinct developmental transitions. Cassava is an important crop for both edible and industrial purposes. Cassava develops storage roots that accumulate starch, providing an important source of staple food in tropical regions. To facilitate cassava breeding, it is important to elucidate how flowering is controlled. Several important genes that control flowering time have been identified in model plants; however, comprehensive characterization of these genes in cassava is still lacking. In this study, we identified genes encoding central flowering time regulators and examined these sequences for the presence or absence of conserved motifs. We found that cassava shares conserved genes for the photoperiodic flowering pathway, including florigen, anti-florigen and its associated transcription factor (GIGANTEA, CONSTANS, FLOWERING LOCUS T, CENTRORADIALIS/TERMINAL FLOWER1 and FD) and florigen downstream genes (SUPRESSOR OF OVEREXPRESSION OF CONSTANS1 and APETALA1/FRUITFUL). We conducted RNA-seq analysis of field-grown cassava plants and characterized the expression of flowering control genes. Finally, from the transcriptome analysis we identified two distinct developmental transitions that occur in field-grown cassava.

Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava (Manihot esculenta Crantz).

BACKGROUND: Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. RESULTS: To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. CONCLUSION: UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

Expression of the CCCH-tandem zinc finger protein gene OsTZF5 under a stress-inducible promoter mitigates the effect of drought stress on rice grain yield under field conditions.

Increasing drought resistance without sacrificing grain yield remains an ongoing challenge in crop improvement. In this study, we report that Oryza sativa CCCH-tandem zinc finger protein 5 (OsTZF5) can confer drought resistance and increase grain yield in transgenic rice plants. Expression of OsTZF5 was induced by abscisic acid, dehydration and cold stress. Upon stress, OsTZF5-GFP localized to the cytoplasm and cytoplasmic foci. Transgenic rice plants overexpressing OsTZF5 under the constitutive maize ubiquitin promoter exhibited improved survival under drought but also growth retardation. By introducing OsTZF5 behind the stress-responsive OsNAC6 promoter in two commercial upland cultivars, Curinga and NERICA4, we obtained transgenic plants that showed no growth retardation. Moreover, these plants exhibited significantly increased grain yield compared to non-transgenic cultivars in different confined field drought environments. Physiological analysis indicated that OsTZF5 promoted both drought tolerance and drought avoidance. Collectively, our results provide strong evidence that OsTZF5 is a useful biotechnological tool to minimize yield losses in rice grown under drought conditions.

Convolutional Neural Net-Based Cassava Storage Root Counting Using Real and Synthetic Images.

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots are usually done manually, or semi-automatically by first segmenting cassava root images. However, occlusion of both storage and fibrous roots makes the process both time-consuming and error-prone. While Convolutional Neural Nets have shown performance above the state-of-the-art in many image processing and analysis tasks, there are currently a limited number of Convolutional Neural Net-based methods for counting plant features. This is due to the limited availability of data, annotated by expert plant biologists, which represents all possible measurement outcomes. Existing works in this area either learn a direct image-to-count regressor model by regressing to a count value, or perform a count after segmenting the image. We, however, address the problem using a direct image-to-count prediction model. This is made possible by generating synthetic images, using a conditional Generative Adversarial Network (GAN), to provide training data for missing classes. We automatically form cassava storage root masks for any missing classes using existing ground-truth masks, and input them as a condition to our GAN model to generate synthetic root images. We combine the resulting synthetic images with real images to learn a direct image-to-count prediction model capable of counting the number of storage roots in real cassava images taken from a low cost aeroponic growth system. These models are used to develop a system that counts cassava storage roots in real images. Our system first predicts age group ('young' and 'old' roots; pertinent to our image capture regime) in a given image, and then, based on this prediction, selects an appropriate model to predict the number of storage roots. We achieve 91% accuracy on predicting ages of storage roots, and 86% and 71% overall percentage agreement on counting 'old' and 'young' storage roots respectively. Thus we are able to demonstrate that synthetically generated cassava root images can be used to supplement missing root classes, turning the counting problem into a direct image-to-count prediction task.

A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta).

BACKGROUND: Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent regulation of storage root (SR) differentiation, due in part to the innate problems of studying storage roots and the lack of a suitable model system for monitoring storage root growth. The research presented here aimed to develop a reliable, low-cost effective system that enables the study of the factors influencing cassava storage root initiation and development. RESULTS: We explored simple, low-cost systems for the study of storage root biology. An aeroponics system described here is ideal for real-time monitoring of storage root development (SRD), and this was further validated using hormone studies. Our aeroponics-based auxin studies revealed that storage root initiation and development are adaptive responses, which are significantly enhanced by the exogenous auxin supply. Field and histological experiments were also conducted to confirm the auxin effect found in the aeroponics system. We also developed a simple digital imaging platform to quantify storage root growth and development traits. Correlation analysis confirmed that image-based estimation can be a surrogate for manual root phenotyping for several key traits. CONCLUSIONS: The aeroponic system developed from this study is an effective tool for examining the root architecture of cassava during early SRD. The aeroponic system also provided novel insights into storage root formation by activating the auxin-dependent proliferation of secondary xylem parenchyma cells to induce the initial root thickening and bulking. The developed system can be of direct benefit to molecular biologists, breeders, and physiologists, allowing them to screen germplasm for root traits that correlate with improved economic traits.

Ground penetrating radar: a case study for estimating root bulking rate in cassava (Manihot esculenta Crantz).

BACKGROUND: Understanding root traits is a necessary research front for selection of favorable genotypes or cultivation practices. Root and tuber crops having most of their economic potential stored below ground are favorable candidates for such studies. The ability to image and quantify subsurface root structure would allow breeders to classify root traits for rapid selection and allow agronomist the ability to derive effective cultivation practices. In spite of the huge role of Cassava (Manihot esculenta Crantz), for food security and industrial uses, little progress has been made in understanding the onset and rate of the root-bulking process and the factors that influence it. The objective of this research was to determine the capability of ground penetrating radar (GPR) to predict root-bulking rates through the detection of total root biomass during its growth cycle. Our research provides the first application of GPR for detecting below ground biomass in cassava. RESULTS: Through an empirical study, linear regressions were derived to model cassava bulking rates. The linear equations derived suggest that GPR is a suitable measure of root biomass (r = .79). The regression analysis developed accounts for 63% of the variability in cassava biomass below ground. When modeling is performed at the variety level, it is evident that the variety models for SM 1219-9 and TMS 60444 outperform the HMC-1 variety model (r2 = .77, .63 and .51 respectively). CONCLUSIONS: Using current modeling methods, it is possible to predict below ground biomass and estimate root bulking rates for selection of early root bulking in cassava. Results of this approach suggested that the general model was over predicting at early growth stages but became more precise in later root development.

Overexpression of an Arabidopsis thaliana galactinol synthase gene improves drought tolerance in transgenic rice and increased grain yield in the field.

Drought stress has often caused significant decreases in crop production which could be associated with global warming. Enhancing drought tolerance without a grain yield penalty has been a great challenge in crop improvement. Here, we report the Arabidopsis thaliana galactinol synthase 2 gene (AtGolS2) was able to confer drought tolerance and increase grain yield in two different rice (Oryza sativa) genotypes under dry field conditions. The developed transgenic lines expressing AtGolS2 under the control of the constitutive maize ubiquitin promoter (Ubi:AtGolS2) also had higher levels of galactinol than the non-transgenic control. The increased grain yield of the transgenic rice under drought conditions was related to a higher number of panicles, grain fertility and biomass. Extensive confined field trials using Ubi:AtGolS2 transgenic lines in Curinga, tropical japonica and NERICA4, interspecific hybrid across two different seasons and environments revealed the verified lines have the proven field drought tolerance of the Ubi:AtGolS2 transgenic rice. The amended drought tolerance was associated with higher relative water content of leaves, higher photosynthesis activity, lesser reduction in plant growth and faster recovering ability. Collectively, our results provide strong evidence that AtGolS2 is a useful biotechnological tool to reduce grain yield losses in rice beyond genetic differences under field drought stress.

Development and field performance of nitrogen use efficient rice lines for Africa.

Nitrogen (N) fertilizers are a major input cost in rice production, and its excess application leads to major environmental pollution. Development of rice varieties with improved nitrogen use efficiency (NUE) is essential for sustainable agriculture. Here, we report the results of field evaluations of marker-free transgenic NERICA4 (New Rice for Africa 4) rice lines overexpressing barley alanine amino transferase (HvAlaAT) under the control of a rice stress-inducible promoter (pOsAnt1). Field evaluations over three growing seasons and two rice growing ecologies (lowland and upland) revealed that grain yield of pOsAnt1:HvAlaAT transgenic events was significantly higher than sibling nulls and wild-type controls under different N application rates. Our field results clearly demonstrated that this genetic modification can significantly increase the dry biomass and grain yield compared to controls under limited N supply. Increased yield in transgenic events was correlated with increased tiller and panicle number in the field, and evidence of early establishment of a vigorous root system in hydroponic growth. Our results suggest that expression of the HvAlaAT gene can improve NUE in rice without causing undesirable growth phenotypes. The NUE technology described in this article has the potential to significantly reduce the need for N fertilizer and simultaneously improve food security, augment farm economics and mitigate greenhouse gas emissions from the rice ecosystem.

OsMADS26 Negatively Regulates Resistance to Pathogens and Drought Tolerance in Rice.

Functional analyses of MADS-box transcription factors in plants have unraveled their role in major developmental programs (e.g. flowering and floral organ identity) as well as stress-related developmental processes, such as abscission, fruit ripening, and senescence. Overexpression of the rice (Oryza sativa) MADS26 gene in rice has revealed a possible function related to stress response. Here, we show that OsMADS26-down-regulated plants exhibit enhanced resistance against two major rice pathogens: Magnaporthe oryzae and Xanthomonas oryzae. Despite this enhanced resistance to biotic stresses, OsMADS26-down-regulated plants also displayed enhanced tolerance to water deficit. These phenotypes were observed in both controlled and field conditions. Interestingly, alteration of OsMADS26 expression does not have a strong impact on plant development. Gene expression profiling revealed that a majority of genes misregulated in overexpresser and down-regulated OsMADS26 lines compared with control plants are associated to biotic or abiotic stress response. Altogether, our data indicate that OsMADS26 acts as an upstream regulator of stress-associated genes and thereby, a hub to modulate the response to various stresses in the rice plant.

Current issues in cereal crop biodiversity.

The exploration, conservation, and use of agricultural biodiversity are essential components of efficient transdisciplinary research for a sustainable agriculture and food sector. Most recent advances on plant biotechnology and crop genomics must be complemented with a holistic management of plant genetic resources. Plant breeding programs aimed at improving agricultural productivity and food security can benefit from the systematic exploitation and conservation of genetic diversity to meet the demands of a growing population facing climate change. The genetic diversity of staple small grains, including rice, maize, wheat, millets, and more recently quinoa, have been surveyed to encourage utilization and prioritization of areas for germplasm conservation. Geographic information system technologies and spatial analysis are now being used as powerful tools to elucidate genetic and ecological patterns in the distribution of cultivated and wild species to establish coherent programs for the management of plant genetic resources for food and agriculture.

Development of disease-resistant rice by optimized expression of WRKY45.

The rice transcription factor WRKY45 plays a central role in the salicylic acid signalling pathway and mediates chemical-induced resistance to multiple pathogens, including Magnaporthe oryzae and Xanthomonas oryzae pv. oryzae. Previously, we reported that rice transformants overexpressing WRKY45 driven by the maize ubiquitin promoter were strongly resistant to both pathogens; however, their growth and yield were negatively affected because of the trade-off between the two conflicting traits. Also, some unknown environmental factor(s) exacerbated this problem. Here, we report the development of transgenic rice lines resistant to both pathogens and with agronomic traits almost comparable to those of wild-type rice. This was achieved by optimizing the promoter driving WRKY45 expression. We isolated 16 constitutive promoters from rice genomic DNA and tested their ability to drive WRKY45 expression. Comparisons among different transformant lines showed that, overall, the strength of WRKY45 expression was positively correlated with disease resistance and negatively correlated with agronomic traits. We conducted field trials to evaluate the growth of transgenic and control lines. The agronomic traits of two lines expressing WRKY45 driven by the OsUbi7 promoter (PO sUbi7 lines) were nearly comparable to those of untransformed rice, and both lines were pathogen resistant. Interestingly, excessive WRKY45 expression rendered rice plants sensitive to low temperature and salinity, and stress sensitivity was correlated with the induction of defence genes by these stresses. These negative effects were barely observed in the PO sUbi7 lines. Moreover, their patterns of defence gene expression were similar to those in plants primed by chemical defence inducers.

A first insight into population structure and linkage disequilibrium in the US peanut minicore collection.

Knowledge of genetic diversity, population structure, and degree of linkage disequilibrium (LD) in target association mapping populations is of great importance and is a prerequisite for LD-based mapping. In the present study, 96 genotypes comprising 92 accessions of the US peanut minicore collection, a component line of the tetraploid variety Florunner, diploid progenitors A. duranensis (AA) and A. ipaënsis (BB), and synthetic amphidiploid accession TxAG-6 were investigated with 392 simple sequence repeat (SSR) marker bands amplified using 32 highly-polymorphic SSR primer pairs. Both distance- and model-based (Bayesian) cluster analysis revealed the presence of structured diversity. In general, the wild-species accessions and the synthetic amphidiploid grouped separately from most minicore accessions except for COC155, and were eliminated from most subsequent analyses. UPGMA analysis divided the population into four subgroups, two major subgroups representing subspecies fastigiata and hypogaea, a third group containing individuals from each subspecies or possibly of mixed ancestry, and a fourth group, either consisting of COC155 alone if wild species were excluded, or of COC155, the diploid species, and the synthetic amphidiploid. Model-based clustering identified four subgroups- one each for fastigiata and hypogaea subspecies, a third consisting of individuals of both subspecies or of mixed ancestry predominantly from Africa or Asia, and a fourth group, consisting of individuals predominantly of var fastigiata, peruviana, and aequatoriana accessions from South America, including COC155. Analysis of molecular variance (AMOVA) revealed statistically-significant (P < 0.0001) genetic variance of 16.87% among subgroups. A total of 4.85% of SSR marker pairs revealed significant LD (at r(2) ≥ 0.1). Of the syntenic marker pairs separated by distances < 10 cM, 11-20 cM, 21-50 cM, and > 50 cM, 19.33, 5.19, 6.25 and 5.29% of marker pairs were found in strong LD (P ≤ 0.01), in accord with LD extending to great distances in self pollinated crops. A threshold value of r(2) > 0.035 was found to distinguish mean r(2) values of linkage distance groups statistically from the mean r(2) values of unlinked markers; LD was found to extend to 10 cM over the entire minicore collection by this criterion. However, there were large differences in r(2) values among marker pairs even among tightly-linked markers. The implications of these findings with regard to the possibility of using association mapping for detection of genome-wide SSR marker-phenotype association are discussed.

Cloning and characterization of resistance gene analogs from under-exploited plant species

Genomic DNA sequences sharing homology with NBS region of resistance gene analogs were isolated and characterized from Pongamia glabra, Adenanthera pavonina, Clitoria ternatea and Solanum trilobatum using PCR based approach with primers designed from conserved regions of NBS domain. The presence of consensus motifs viz., kinase 1a, kinase 2, kinase 3a and hydrophobic domain provided evidence that the cloned sequences may belong to the NBS-LRR gene family. Conservation of tryptophan as the last residue of kinase-2 motif further confirms their position in non-TIR NBS-LRR family of resistance genes. The Resistance Gene Analogs (RGAs) cloned from P. glabra, A. pavonina, C. ternatea and S. trilobatum clustered together with well- characterized non-TIR-NBS-LRR genes leaving the TIR-NBS-LRR genes as a separate cluster in the average distance tree constructed based on BLOSUM62. All the four RGAs had high level of identity with NBS-LRR family of RGAs deposited in the GenBank. The extent of identity between the sequences at NBS region varied from 29 percent (P. glabra and S. trilobatum) to 78 percent (A. pavonina and C. ternatea), which indicates the diversity among the RGAs.