Major Crops Research Articles

Finger millet: a hero in the making to combat food insecurity.

Climate change and population growth pose challenges to food security. Major crops such as maize, wheat, and rice are expected to face yield reductions due to warming in the coming years, highlighting the need for incorporating climate-resilient crops in agricultural production systems. Finger millet (Eleusine coracana (L.) Gaertn) is a nutritious cereal crop adapted to arid regions that could serve as an alternative crop for sustaining the food supply in low rainfall environments where other crops routinely fail. Despite finger millet's nutritional qualities and climate resilience, it is deemed an "orphan crop," neglected by researchers compared to major crops, which has hampered breeding efforts. However, in recent years, finger millet has entered the genomics era. Next-generation sequencing resources, including a chromosome-scale genome assembly, have been developed to support trait characterization. This review discusses the current genetic and genomic resources available for finger millet while addressing the gaps in knowledge and tools that are still needed to aid breeders in bringing finger millet to its full production potential.

Drought response revealed by chromatin organization variation and transcriptional regulation in cotton

BackgroundCotton is a major world cash crop and an important source of natural fiber, oil, and protein. Drought stress is becoming a restrictive factor affecting cotton production. To facilitate the development of drought-tolerant cotton varieties, it is necessary to study the molecular mechanism of drought stress response by exploring key drought-resistant genes and related regulatory factors.ResultsIn this study, two cotton varieties, ZY007 (drought-sensitive) and ZY168 (drought-tolerant), showing obvious phenotypic differences under drought stress, were selected. A total of 25,898 drought-induced genes were identified, exhibiting significant enrichment in pathways related to plant stress responses. Under drought induction, At subgenome expression bias was observed at the whole-genome level, which may be due to stronger inhibition of Dt subgenome expression. A gene co-expression module that was significantly associated with drought resistance was identified. About 90% of topologically associating domain (TAD) boundaries were stable, and 6613 TAD variation events were identified between the two varieties under drought. We identified 92 genes in ZY007 and 98 in ZY168 related to chromatin 3D structural variation and induced by drought stress. These genes are closely linked to the cotton response to drought stress through canonical hormone-responsive pathways, modulation of kinase and phosphatase activities, facilitation of calcium ion transport, and other related molecular mechanisms.ConclusionsThese results lay a foundation for elucidating the molecular mechanism of the cotton drought response and provide important regulatory locus and gene resources for the future molecular breeding of drought-resistant cotton varieties.

Effect of Planting Density and Nitrogen Levels on the Yield Attributing Characters of Fodder Maize (Shalimar Fodder Maize-1)

Maize (Zea mays L.) is widely known as a ready-made fodder crop. Maize is the third major cereal crop in the world, and in India, it ranks third. Maize has been an important cereal crop owing to its highest production potential and wider adaptability to varied agroclimatic conditions, hence being called the ‘Queen of Cereals’. The production potential of forage maize can be altered with changes in agronomic practices. The present study was undertaken with the objective of improving the yield attributes and quality traits of forage maize (Shalimar Fodder Maize-1). The experiment consisted of two factors, viz., three planting densities (viz., D1= 30cm x 10cm, D2= 40cm x 10cm, and D3= 50cm x 10cm) as main plots and five nitrogen levels (viz., N1= Control, N2= 75% RDN (recommended dose of nitrogen), N3= 100% RDN, N4= 125% RDN, and N5= 150% RDN) as subplot treatments with three replications, laid out in split plot design (SPD). The parameters such as yield attributes, and quality parameters were significantly higher when plants were grown at a density of 30 cm x 10 cm with a nitrogen level of 150 % RDN.

A Review of Factors Affecting Pod Shattering in Soybean (Glycine max)

Soybean is an important crop in the world cultivated for its oil and protein content. It is a significant component of the small holder cropping system and has the potential to become a major crop produced in Africa. However, its productivity is hampered by a number of biotic and abiotic factors. Among the important biotic factors affecting the yield of soybean is pod shattering. Pod shattering is the opening of mature pods along the dorsal or ventral sutures (located along the length of the pod) when the crop matures or during harvesting resulting in seed dispersal. It is a quantitative trait that is influenced by one major gene and a few minor genes and is also highly heritable. It can cause yield losses of up to100%. Apart from causing yield losses, pod shattering may pose a challenge to the crop rotations in the following seasons as seeds from shattered pods tend to emerge as volunteer weeds. There are a number of factors that are linked to pod shattering. An overview of the morphological, anatomical, environmental and genetic aspects associated with pod shattering in soybean is discussed in this review. Understanding all the factors underlying pod shattering in depth is key in breeding soybean varieties that delay to shatter. This can help breeders in knowing which approach to take in breeding for soybeans with pods that delay to shatter. Breeding strategies can focus on manipulating morphological, biochemical and anatomical traits.

Agronomic innovations for enhancement of zinc content in pulses: A review

The rapid pace of climate change is adversely affecting crop yields, both quantitatively and qualitatively, with staple cereals requiring high inputs, posing ecological, economic, and nutritional risks. This shift contributes to a global concern known as ‘hidden hunger’, where micronutrient malnutrition has severe health repercussions, particularly in developing nations. The important nutrient deficiencies like zinc (Zn) in major food crops, posing a significant public health threat. In this context, pulse crops emerge as a promising solution within low-input farming systems, known for their suitability and nutritional richness. These crops can play a pivotal role in climate-resilient food systems. This review deals with the untapped potential of pulses as a crucial component in low-input food-production systems, emphasizing the need for zinc biofortification in pulses to sustain human nutrition.

Genome-wide identification of the plant homeodomain-finger family in rye and ScPHD5 functions in cold tolerance and flowering time.

111 PHD genes were newly identified in rye genome and ScPHD5's role in regulating cold tolerance and flowering time was suggested. Plant homeodomain (PHD)-finger proteins regulate the physical properties of chromatin and control plant development and stress tolerance. Although rye (Secale cereale L.) is a major winter crop, PHD-finger proteins in rye have not been studied. Here, we identified 111 PHD genes in the rye genome that exhibited diverse gene and protein sequence structures. Phylogenetic tree analysis revealed that PHDs were genetically close in monocots and diverged from those in dicots. Duplication and synteny analyses demonstrated that ScPHDs have undergone several duplications during evolution and that high synteny is conserved among the Triticeae species. Tissue-specific and abiotic stress-responsive gene expression analyses indicated that ScPHDs were highly expressed in spikelets and developing seeds and were responsive to cold and drought stress. One of these genes, ScPHD5, was selected for further functional characterization. ScPHD5 was highly expressed in the spike tissues and was localized in the nuclei of rye protoplasts and tobacco leaves. ScPHD5-overexpressing Brachypodium was more tolerant to freezing stress than wild-type (WT), with increased CBF and COR gene expression. Additionally, these transgenic plants displayed an extremely early flowering phenotype that flowered more than two weeks earlier than the WT, and vernalization genes, rather than photoperiod genes, were increased in the WT. RNA-seq analysis revealed that diverse stress response genes, including HSPs, HSFs, LEAs, and MADS-box genes, were also upregulated in transgenic plants. Our study will help elucidate the roles of PHD genes in plant development and abiotic stress tolerance in rye.

Effect of Drying on The Physical Characteristics of Mizo Chilli (Bird’s Eye Chilli) and Study of Its Drying Kinetics

Mizo chilli (Bird’s eye chilli), locally known as Hmarchate or Vaihmarchate is one of the hottest chillies in the world and a major spice crop of Mizoram. Traditional sun-drying at temperatures around 33±2℃ and hot air oven drying at 40°C, 50℃ and 60℃ was carried out using the green chillies (grade B and C) having lengths of around 2.5 cm–4 cm. The initial moisture content of the fresh chillies was 78±5% (wb) having a water activity of 0.981±0.017, and it was dried to a final moisture content of 6±2% (wb). The time required for sun drying was around 30 h which was done during the daytime of bright sunny days. The hot air oven drying at 40°C, 50℃, and 60℃ were completed in 26 h, 24 h, and 23 h respectively. The color variation of the samples was measured using a hunter lab calorimeter. The water activity (aw) of the sun-dried samples was 0.407±0.004 and that of the hot air-dried samples was 0.551±0.003. The sun drying gave a better appearance than the hot air drying. Studying the drying behavior and kinetics of the samples, the Page model was best fitted for sun drying while the Lewis model was found to be best fitted for hot air oven drying.

Enhanced radiation use efficiency and grain filling rate as the main drivers of grain yield genetic gains in the CIMMYT elite spring wheat yield trial

Common wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world’s human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018–2019 to 2020–2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha−1 (37th ESWYT), representing a cumulative increase of 1.12 t ha−1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha−1 year−1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m−2 year−1 (p < 0.01), 0.074 g m−2 year−1 (p < 0.05), and 0.017 g MJ−1 year−1 (p < 0.001) for BM, GFR, and RUE_total, respectively. Grain weight per spike exhibited a positive but no significant trend (0.014 g year−1, p = 0.07), whereas a negative tendency for DTH was observed (− 0.43 days year−1, p < 0.001). Analysis of the top ten highest-yielding genotypes revealed differential GY-associated trait contributions, demonstrating that improved GY can be attained through different mechanisms and indicating that no single trait criterion is adopted by CIMMYT breeders for developing new superior lines. We conclude that CIMMYT’s Bread Wheat Breeding Program has continued to deliver adapted and more productive wheat genotypes to National partners worldwide, mainly driven by enhancing RUE_total and GFR and that future yield increases could be achieved by intercrossing genetically diverse top performer genotypes.

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

Bemisia tabaci, (Gennadius, 1889) occurrence on soybean cultivars in Brazil

Soybeans are a major grain crop in Brazil, crucial for global food security. However, it faces significant threats from pests like the whitefly, Bemisia tabaci (Gennadius, 1889) (Hemiptera: Aleyrodidae). Currently, whitefly control relies heavily on chemical insecticides, leading to an escalation in sprays and the emergence of resistant populations. This study aimed to assess the incidence of whiteflies on different soybean cultivars throughout the crop cycle. Adult whiteflies were counted weekly on soybean leaves, while nymphs were evaluated on leaflets in laboratory conditions. The results revealed that cultivars DM 5958, BMX ZEUS, M 5947, TMG 7262, and DM 66i68 exhibited the highest incidence of B. tabaci.

Animal Detection based Smart Farming in Animal Repellent Using AI and Deep Learning

The combination of artificial intelligence (AI) and deep learning in agriculture has ushered in a new era in agriculture with new solutions designed to solve many problems. This paper presents an animal killing system for smart agriculture that uses artificial intelligence and deep learning to reduce the growing problem of animal damage. As the world population continues to grow, increasing food availability is important; Therefore, it is critical to protect crops from wild animals and pests. Deforestation due to livestock farming has become one of the largest human-wildlife conflicts due to human interference with habitats and deforestation. Wild animals can kill farmers working in the fields, causing major crop losses. Farmers suffered huge crop losses due to wild animals such as elephants, wild boars and deer attacking agriculture. Protecting crops from wild animals is one of the biggest concerns of today's farmers. There are many ways to solve this problem, both lethal (such as shooting and trapping) and non-lethal. (such as railings, pesticides, organic matter, netting or electric fencing). The sensor rotates around the lens and uses DCNN software to detect the target. If an animal is found, it sends a message to the Animal Repellent Module with information on the type of ultrasound that should be produced based on the animal. The development of drones with controlled flight control, as well as lightweight and powerful hyperspectral snapshot cameras that can be used to calculate crop biomass growth and fertilization status, responds to complex agricultural management strategies. KEYWORDS: Animal detection, VGG-Net, Bi-LSTM, convolutional neural network, activity recognition, video surveillance, wild animal monitoring, alert system.

A Dynamic Evolutionary Analysis of the Vulnerability of Global Food Trade Networks

The global food trade network (FTN) is a critical infrastructure for achieving the Sustainable Development Goals (SDGs). The FTN’s vulnerability to geopolitical conflicts, public health crises, and climate change events directly impacts food security and the ability to meet the SDGs. This study aims to analyze the dynamic evolution of the vulnerability of FTN, focusing on the period from 2000 to 2022, to aim for strategies for enhancing the resilience and sustainability of the global food system. Based on complex network analysis, we examine the structural characteristics and evolution of FTN for four major crops: soybeans, wheat, rice, and maize. We identify a trend towards increased network density and regionalization, with a decline in average shortest path length (ASPL) and an increase in the average clustering coefficient (ACC). These changes indicate a shift towards a more interconnected and resilient FTN in response to various shocks, including the COVID-19 pandemic and the Russia–Ukraine conflict. The findings suggest that the global FTN has adapted to increase resilience, which is essential for achieving the SDGs related to food security and sustainable development. The study’s insights can guide policy interventions to further strengthen the network against future shocks and promote global food security.

In vitro and in vivo antifungal efficacy of individual and consortium Bacillus strains in controlling potato black scurf and possible development of spore-based fungicide

Potato is a major crop whose yield and quality are impacted by the pathogenic fungus Rhizoctonia solani. Here, a control approach based on the use of endophytic bacillus spores and bacterial consortia was assessed as an innovative solution to improve tuber productivity and health. Endophytic potato bacteria extracted from several organs (leaves, stems and roots) evaluated for their antagonistic activity against R. solani by the double culture technique and their ability to rot potato slices. Additionally, the ability of Bacillus strains to generate hydrolytic enzymes and produce lipopeptides (using ESI-MS/MS mass spectrometry) was assessed. Then, the effectiveness of selected bacterial strains, either individually or in consortium (after carrying out compatibility tests) in the suppression of R. solani was evaluated in the greenhouse. Six endophytes not affecting potato tubers and having an antagonistic effect against R. solani were identified: B. halotolerans SpS5, B. velezensis KnL15, B. aryabhattai FaR1, B. amyloliquefaciens LiR9, B. haynesii ReR10 which produce several types and combinations of lipopeptides belonging to different families (iturins, fengycins and surfactins) as well as hydrolytic enzymes (chitinase, protease and cellulase). The vegetative and sporulating strain SpS5, as well as the 5 other Bacillus and their Sps5-based consortia, were found to be very effective in increasing the yield of healthy, high-quality tubers and reducing disease severity. Based on the results mentioned above, a microbial fungicide from the spores of (i) B. halotolerans SpS5 for which we sequenced the whole genome or (ii) bacterial consortia could be developed to combat black scurf potato and other fungal infections.

Random Forest Spatial Interpolation Techniques for Crop Yield Estimation at District Level

General Crop Estimation Surveys (GCES) based on Crop Cutting Experiments (CCEs) are conducted for estimation of crop yield following random sampling approach for almost all major crops. About 13 lakh CCEs are conducted every year which has now increased rapidly due to the Pradhan Mantri Fasal Bima Yojana (PMFBY) which is yield based insurance scheme. As suggested by Ministry of Agriculture and Farmers’ Welfare (MoA&amp;FW), this number needs to be reduced drastically by developing sampling procedures based on the use of advanced technologies and advanced survey techniques for crop yield estimation. In this study, an attempt has been made to develop crop yield estimation procedures using Random Forest Spatial Interpolation (RFSI) technique including the spatial variables like spatial distance and nearest neighbours as covariates. RFSI is one of the most adaptable and user-friendly interpolation techniques, as well as one of the fastest in large training datasets. Estimates of yield of wheat were obtained for all the six tehsils of Barabanki district using the estimator under stratified two stage sampling technique. The district level estimates were also obtained by pooling area under wheat crop in each tehsil along with the district level estimate of crop yield, estimate of variance, estimate of standard error (SE) and percentage SE (%SE) of these estimates were also computed in order to make comparison. The results of this study suggest that the estimates derived using RFSI are comparable to kriging and superior to inverse distance weighting (IDW) for the prediction of yield at unknown locations using distance and nearest neighbours.

Remote Sensing-Based Monitoring of Cotton Growth and Its Response to Meteorological Factors

Cotton is an important economic crop and strategic resource. Monitoring its growth and analysing its response to meteorological factors are crucial for field management and yield estimation. This study selects the primary cotton-producing regions in northern Xinjiang as the study area. Firstly, using the Google Earth Engine cloud platform, the Cotton Mapping Index (CMI) was utilised to extract cotton planting areas from 2019 to 2023. Secondly, Sentinel-2A data were used to calculate the NDVI of cotton during the growing season and analyse its variation characteristics. Finally, correlation, lag, and partial correlation analyses were conducted between cotton NDVI and meteorological factors, including effective accumulated temperature, wind speed, precipitation, and solar shortwave radiation, to explore the response relationship. The results indicate the following: (1) The optimal classification threshold of CMI in the study area was determined to be 0.74, which was applied to extract cotton planting areas over the years. The overall classification accuracy achieved was 84.85%. The R2 value for the cotton area extracted by CMI compared to the cotton planting area in the statistical yearbook data is 0.98, with an average relative error of 16.84%. CMI’s classification use effectively distinguishes cotton from other major crops, such as wheat and corn, in the study area. Compared with different classification methods, CMI is more convenient and efficient for extracting cotton planting areas, contributing significantly to yield estimation and management. (2) We found that from 2019 to 2023, some fields were planted with cotton yearly. In order to prevent land degradation, a crop rotation system should be implemented, in which cotton rotates with other crops to reduce the rate of soil nutrient loss and achieve sustainable agricultural development. (3) NDVI can effectively monitor the spatiotemporal changes and regional variations in cotton growth. Sentinel-2 multi-spectral imagery possesses high spatial and temporal resolution, enabling effective monitoring of cotton growth, provision of cotton growth data for field managers, and application in cotton production management. Additionally, cotton yield estimation can be achieved by comparing the overall growth of cotton across different years. (4) Cotton NDVI exhibits a strong correlation with effective accumulated temperature and solar radiation, with the majority passing the significance test, suggesting a significant promotion effect on cotton growth by accumulated temperature and solar radiation. In cotton cultivation management, attention should be directed toward monitoring changes in accumulated temperature and solar radiation. Moreover, NDVI changes in response to solar radiation exhibit a certain lag. The correlation between NDVI and precipitation is low, likely attributed to local cotton cultivation primarily relying on drip irrigation. Cotton NDVI is negatively correlated with wind speed. Cotton planting should consider weather changes and take corresponding preventive management measures. The research results have significant reference value for monitoring cotton growth, disaster prevention, and sustainable agricultural development.

MicroRNA (miRNA) profiling of maize genotypes with differential response to Aspergillus flavus implies zma-miR156–squamosa promoter binding protein (SBP) and zma-miR398/zma-miR394–F -box combinations involved in resistance mechanisms

Maize (Zea mays), a major food crop worldwide, is susceptible to infection by the saprophytic fungus Aspergillus flavus that can produce the carcinogenic metabolite aflatoxin (AF) especially under climate change induced abiotic stressors that favor mold growth. Several studies have used “-omics” approaches to identify genetic elements with potential roles in AF resistance, but there is a lack of research identifying the involvement of small RNAs such as microRNAs (miRNAs) in maize-A. flavus interaction. In this study, we compared the miRNA profiles of three maize lines (resistant TZAR102, moderately resistant MI82, and susceptible Va35) at 8 h, 3 d, and 7 d after A. flavus infection to investigate possible regulatory antifungal role of miRNAs. A total of 316 miRNAs (275 known and 41 putative novel) belonging to 115 miRNA families were identified in response to the fungal infection across all three maize lines. Eighty-two unique miRNAs were significantly differentially expressed with 39 miRNAs exhibiting temporal differential regulation irrespective of the maize genotype, which targeted 544 genes (mRNAs) involved in diverse molecular functions. The two most notable biological processes involved in plant immunity, namely cellular responses to oxidative stress (GO:00345990) and reactive oxygen species (GO:0034614) were significantly enriched in the resistant line TZAR102. Coexpression network analysis identified 34 hubs of miRNA-mRNA pairs where nine hubs had a node in the module connected to their target gene with potentially important roles in resistance/susceptible response of maize to A. flavus. The miRNA hubs in resistance modules (TZAR102 and MI82) were mostly connected to transcription factors and protein kinases. Specifically, the module of miRNA zma-miR156b-nb – squamosa promoter binding protein (SBP), zma-miR398a-3p – SKIP5, and zma-miR394a-5p – F-box protein 6 combinations in the resistance-associated modules were considered important candidates for future functional studies.

Is the boom in staple crop production attributed to expanded cropland or improved yield? A comparative analysis between China and India

The characteristics of cropland development and the dynamics of food production in China and India, the world's largest agricultural and most populous countries, are of great importance to global food security. However, there is a notable lack of a thorough comparison between China and India in this regard. Here, we systematically compare the differences between China and India using cropping intensity and crop production data, including cropland area, harvested area, total staple crop (i.e., cereal crops, tuber crops and pulse crops) production and yield capacity. The results are mainly as follows: (1) Both China and India experienced an increasing trend in cropland area and harvested area from 2001 to 2021, especially notable in India. In China, the cropland area and harvested area increased by 11.76 % and 14.36 %, respectively, while in India, they witnessed a more substantial increase of 31.10 % and 49.32 %, respectively. (2) The cropping intensity underwent significant transformations, primarily shifting between non-cropland, single-cropping, and double-cropping. Northwestern China exhibited a clear trend of non-cropland converting to single-cropping, whereas northeastern China showed a distinct pattern of single-cropping changing to non-cropland. The interconversion between single-cropping and double-cropping was also frequently observed in the main food-producing regions. In India, the cropland expansion and the adoption of double-cropping are highly pronounced, extending widely across most of the country. (3) From 2001 to 2021, the total staple crop production in China and India increased by 34.12 % and 55.81 %, respectively. Despite the rapid growth in India's total staple crop production, it still amounts to only about half of China's. The major crops production also showed different trends, China's cereal crops production increased significantly, while tuber and pulse crops production declined, and India's production of cereal, tuber, and pulse crops has all increased (4) China's yield capacity has increased by 17.28 %, while India's has only grown by 4.35 %. Despite the rapid increase in India's total staple crop production, the yield gap with China has widened. The boost in China's total staple crop production mainly resulted from improved yield capacity, whereas India relied more on the cropland area expansion, especially the increase in harvested area. Our comprehensive comparison of China and India in cropland development and staple crop production contributes to a deep understanding of the differences in agricultural production between the two countries, and provides lessons for global food security and sustainable agricultural development.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Decadal Performance of Major Kharif Pulse Crops in Madhya Pradesh: In Context of Growth, Decomposition and Instability Analysis

The study purpose examined the growth, decomposition and instability of major kharif pulses i. e. pigeon pea, green gram and black gram in Madhya Pradesh. The study was based on secondary data. The data of area, production, and productivity of selected pulse crops have been taken for 30 years from 1992-93 to 2021-22. The study period was divided into four periods i.e. period-I (1992-93 to 2001-02), period-II (2002-03 to 2011-12), period- III (2012-13 to 2021-22) and overall period (1992-93 to 2021-22). Three different analyses had been carried out in the study viz. (a) Compound annual growth rates, (b) Decomposition analysis and (c) Instability analysis. It was revealed from the outcomes that during the entire study period, a mixed growth pattern was observed in the area, production and productivity of pigeon pea and green gram while significant and positive growth was noticed in area, production and productivity of black gram with the magnitude of 4.31, 6.19 and 1.80 percent, respectively in Madhya Pradesh. During overall study period, area expansion was major instrument to increase the production of pigeon pea and black gram. The highest variability was noticed production of all selected crops during period – I, II, III and entire study period.

PH Stabilized NH4+-Fed Nutrition Promotes Higher B Uptake and Plant Growth in Rapeseed (Brassica napus L.) by the Upregulation of B Transporters

AbstractRapeseed (Brassica napus L.) is a major oilseed crop with high boron (B) requirements. In hydroponic cultivation, B uptake is highly responsive to the media conditions (e.g., pH). Different N sources, such as NH4+ and NO3−, directly affect the media pH. To date, the underlying mechanisms, the role of the buffering at constant N-sources by separating the effects of pH and/or sources of N-form, for the uptake of B remain indistinct under hydroponic cultivation, particularly in rapeseed. Two identical experiments were conducted using (NH4)2SO4 and Ca(NO3)2 as sole N-sources each set were subjected to four treatments viz. non-buffered, buffered pH 5.0, 6.0, and 7.0 at low (1 µM) and high (100 µM) B for 5 days. The novelty of this study demonstrates that NH4+-fed plants prefer buffered pH (6 and 7) for promoting higher plant growth, which was in line with the B concentration, while NO3−-fed plants were not influenced. These aforesaid findings were further supported by the upregulation of BnaNIP5;1 and BnaBOR1;2 in the root and higher expression of BnaNIP5;1 in shoots with buffered pH in NH4+-fed plants. According to our findings, in NH4+-based buffering, B transport channel protein BnaNIP5;1 (through diffusion) facilitated B uptake and distribution from root to shoot while BnaBOR1;2 mediated active uptake of B was in root under low supply of B. In a nutshell, pH buffering in hydroponics is very important in NH4+-based but not in NO3−-based nutrition for the uptake of boron and growth of the rapeseed plants.