Novel Sequencing and Genomic Technologies Revolutionized Rice Genomic Study and Breeding

Rice is one of the most important food crops worldwide. Population growth and climate change posed great challenges for further rice production. In the past decade, we have witnessed an explosive development in novel sequencing and genomic technologies. These technologies have been widely applied in rice genomic study and improvement processes, and contributed greatly to increase the efficiency and accuracy of rice breeding. On the other hand, novel sequencing and genomic technologies also promote the shift of breeding schemes from conventional field selection processes to genomic assisted breeding. These technologies have revolutionized almost every aspect of rice study and breeding. Here, we systematically sorted out and reviewed the progress and advancements of sequencing and genomic technologies. We further discussed how these technologies were incorporated into rice breeding practices and helped accelerate the rice improvement process. Finally, we reflected on how to further utilize novel sequencing and genomic technologies in rice genetic improvement, as well as the future trends of advancement for these technologies. It can be expected that, as the sequencing and genomic technologies will develop much more quickly in the future, and be combined with novel bioinformatics tools, rice breeding will move forward into the genomic assisted era.

A CNN-Based Method for Counting Grains within a Panicle

The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.

Participatory Rice Breeding Based on The Concept of Sustainable Agriculture Region

<p>The Rice farming system has long been implemented by a typical top-down approach. The degree of creativity and autonomy of farmers has been determined by the continuous external inputs. The main challenge of rice breeding programs is to improve the quality of varieties to be developed in a specific environment and acceptable to the user community. The adoption of new varieties was limited because the breeding process has not taken into account the farmers' preferences. Another obstacle that often arises was that varieties are less adaptive in specific conditions. The variety's productivity depends on farmers' knowledge, facilities, and resource management. This paper explores the principles of participatory rice breeding and its application with a comprehensive approach that aims to encourage farmer empowerment in assembling superior varieties and providing seeds independently. Participatory breeding programs are offered as a solution in understanding the needs of sustainable agriculture. The farmers' involvement serves to capture preferences and selection of lines with high yield potential and were environmentally adaptive. Collaboration was carried out through testing the lines on farmers' land. Decentralization breeding also pays attention to the agroecological paradigm in the scale of the agricultural region. Implementation of agricultural region development serves to preserve sustainable agricultural resources and the environment. The farmers' active participation in the agricultural region has a positive impact on ecosystem sustainability, biodiversity, and environmental conservation for the future. Participatory rice breeding through integrated policies contributes to improving farmers' welfare and realizing environmental sustainability through agricultural region management.</p><p><strong>Key words: </strong>Rice, breeding, participatory, collaboration, varieties</p><p> </p><p><strong>Abstrak</strong></p><p><strong>PEMULIAAN PADI SECARA PARTISIPATIF BERBASIS KONSEP KAWASAN PERTANIAN BERKELANJUTAN</strong></p><p>Sistem pertanian padi telah lama diterapkan dengan pendekatan <em>top-down</em> yang khas. Tingkat kreativitas dan otonomi petani ditentukan oleh input eksternal secara terus menerus. Tantangan utama program pemuliaan padi ialah meningkatkan kualitas varietas untuk dikembangkan di lingkungan khusus dan dapat diterima oleh masyarakat pengguna. Adopsi varietas baru terbatas karena proses pemuliaan belum memperhatikan preferensi petani. Kendala lain yang sering muncul yaitu varietas kurang adaptif pada lingkungan spesifik. Produktivitas varietas bergantung pada pengetahuan petani, fasilitas, dan pengelolaan sumber daya. Makalah ini menggali prinsip pemuliaan padi secara partisipatif dan penerapannya dengan pendekatan komprehensif yang bertujuan utuk mendorong pemberdayaan petani dalam perakitan varietas unggul dan penyediaan benih secara mandiri. Program pemuliaan partisipatif ditawarkan sebagai solusi dalam memahami kebutuhan pertanian berkelanjutan. Keterlibatan petani berfungsi untuk menjaring preferensi dan seleksi galur dengan potensi hasil tinggi dan adaptif lingkungan. Kolaborasi dilakukan melalui uji galur di lahan petani. Pemuliaan desentralisasi juga memperhatikan paradigma agroekologi dalam skala kawasan pertanian. Implementasi pembangunan kawasan pertanian berfungsi melestarikan sumber daya dan lingkungan pertanian berkelanjutan. Partisipasi aktif petani di kawasan pertanian berdampak positif terhadap kelestarian ekosistem, keanekaragaman hayati, dan konservasi lingkungan bagi masa depan. Pemuliaan padi partisipatif melalui kebijakan terintegrasi berkontribusi pada peningkatan kesejahteraan petani dan mewujudkan kelestarian lingkungan melalui pengelolaan kawasan pertanian.</p><p><strong>Kata kunci: </strong>Padi, pemuliaan, partisipatif,<strong> </strong>kolaborasi, varietas</p>

A New Strategy for Specific Qualities of Waxy Rice Breeding by Analyzing Physicochemical Properties Between Five Waxy Mutants and Corresponding Their Wild Types

Waxy rice is an essential mutant type of rice, which quality is controlled by amylopectin fine structure and apparent amylose content (AAC). The influence of amylopectin structure and AAC on quality and the waxy rice can be obtained by editing the Waxy (Wx) gene have been elucidated. However, the quality of waxy rice cannot be predicted before breeding, especially how to determine the quality of waxy (wx) mutants by wild types (WT) quality remains unclear. Herein, the quality of waxy rice has been successfully predicted through analyzing the association in physicochemical properties before and after Wx gene knockout. We demonstrated that the higher amylose WT would obtain higher amylose wx mutants, and wx mutants were endowed gelatinization temperature, amylopectin chain ratio and agronomic traits similar to WT. These data indicate that the quality of wild varieties played a decisive role in waxy rice breeding. Overall, we provide a new strategy for the specific quality breeding of waxy rice, which can get waxy rice of prescribed quality and contribute to expanding the particular type of waxy rice germplasm resources.

Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

High-Quality Genomes and High-Density Genetic Map Facilitate the Identification of Genes From a Weedy Rice

Genes have been lost or weakened from cultivated rice during rice domestication and breeding. Weedy rice (Oryza sativa f. spontanea) is usually recognized as the progeny between cultivated rice and wild rice and is also known to harbor an gene pool for rice breeding. Therefore, identifying genes from weedy rice germplasms is an important way to break the bottleneck of rice breeding. To discover genes from weedy rice germplasms, we constructed a genetic map based on w-hole-genome sequencing of a F2 population derived from the cross between LM8 and a cultivated rice variety. We further identified 31 QTLs associated with 12 important agronomic traits and revealed that ORUFILM03g000095 gene may play an important role in grain length regulation and participate in grain formation. To clarify the genomic characteristics from weedy rice germplasms of LM8, we generated a high-quality genome assembly using single-molecule sequencing, Bionano optical mapping, and Hi-C technologies. The genome harbored a total size of 375.8 Mb, a scaffold N50 of 24.1 Mb, and originated approximately 0.32 million years ago (Mya) and was more closely related to Oryza sativa ssp. japonica. and contained 672 unique genes. It is related to the formation of grain shape, heading date and tillering. This study generated a high-quality reference genome of weedy rice and high-density genetic map that would benefit the analysis of genome evolution for related species and suggested an effective way to identify genes related to important agronomic traits for further rice breeding.