A pot experiment was conducted to investigate the dose-dependent response of tomato (Solanum lycopersicum L.) to foliar application of nano calcium oxide (nano CaO). The study included 13 treatments comprising 11 nano CaO concentrations (100–1500 ppm), a calcium nitrate (CaNO3) treatment and an untreated control, laid out in a completely randomised design (CRD) with 3 replications. Foliar sprays were applied at 30 and 45 DAT. Most traits differed significantly (p < 0.001), showing a hormesis-type response with optimum performance at 600 ppm nano CaO. The 600 ppm treatment consistently produced the tallest plants, highest dry weight, soil plant analysis development (SPAD) values, fruit size, yield (2.23 kg plant-1), shelf life (14.5 days) and fruit firmness (3.21 kg cm-2), while recording the lowest percent of physiological loss in weight (10.1 after 12 days). Compared with calcium nitrate, nano CaO at 600 ppm increased yield by 18.62 % using less than one-third of the calcium concentration. Quadratic regression predicted an optimum nano CaO concentration of 550–635 ppm, corroborating the observed optimum, while correlation analysis identified fruit volume and plant dry weight as the strongest predictors of yield and post-harvest storability. These findings indicate that foliar application of nano CaO at approximately 600 ppm can improve tomato growth, yield and postharvest storability at a substantially lower calcium concentration than calcium nitrate, while the decline in performance at higher concentrations emphasises the need for dose optimisation.
Plant diseases are a major threat to global food security, causing an estimated annual economic loss of approximately USD 220 billion. Even though deep learning models excel at image-based disease detection, the requirement for many labelled images with a quantitative prediction uncertainty can be problematic in real-world agricultural applications. Bayesian neural networks (BNNs) overcome these limitations by incorporating prior knowledge from plant pathology, symptom morphology and environmental factors within a probabilistic learning framework. This approach not only improves prediction accuracy but also yields calibrated uncertainty estimates alongside each prediction. This review synthesises recent studies on BNNs, focusing on domain-knowledge, data-driven and functional priors for plant disease detection. The evidence gained from the studies reviewed indicates that BNNs consistently outperform deterministic models, especially in low-data regimes, as may be the case for smallholder farming characterised by heterogeneous environments and limited annotation resources. The review also highlights challenges related to computational complexity, prior selection and edge deployment, while outlining future research opportunities for robust agricultural artificial intelligence (AI) systems.
Vegetables are the major food source for nutritional security for the teeming millions across the globe. Unfortunately, the growth and development of vegetable crops face yield reduction due to the infestation of notorious sedentary endoparasites, root-knot nematodes (RKN, Meloidogyne spp.). The RKN substantially harm the vegetable crops (20–100 %) culminating in huge economic losses to the growers, becoming a serious threat to the world agricultural sector. Chemical-based nematicides are effective to a certain extent but are costly, unsafe for humans and animals and subject to various regulations. Hence, it is crucial to use green and sustainable approaches for managing RKN infection. Approaches such as cultural practices, application of cover/antagonistic crops, use of organic amendments, nanoparticles (NPs) and chemical nematicides have been deployed for management of RKN. However, application of biocontrol agents is a safer, more reliable, cost-effective and promising alternative method for management of RKN. These biocontrol agents not only improve plant health but also facilitate plants to withstand various biotic stresses by inducing systemic resistance. Novel approaches, such as transgenic technologies, omics-based strategies, and integrated management of root-knot nematodes (RKNs), are receiving increasing attention.Hence, the present review focuses on the challenges associated with RKN management and provides explicit information on traditional and modern approaches of RKN management. It also emphasises sustainable biocontrol of RKN through fungi and bacteria, for food security.
In recent years, farmers have started growing Santalum album L. in states of India like Maharashtra. This has brought a major change to local agroforestry. In this research we wanted to understand how three different tree spacings (3.05 x 3.05 m, 3.66 x 3.66 m and 3.66 x 4.57 m) alter actual tree growth and carbon (C) storage in Western Maharashtra. We made sure to track the exact land conditions across our 24 study sites. The soil types were neutral to moderately alkaline Vertic and Typic Haplustepts with a pH of 7.1–8.1 and the 10 year local rainfall stayed between 112.03 and 233.45 mm. Field data were gathered on standard traits like tree height, thickness (DBH) and wood volume for two age groups (1–5 years and 6–10 years). To find the final biomass weights, we applied the standard equations along with the scaling methods. All collected datasets were carefully checked using a two-way analysis of variance (ANOVA). The field records showed a very clear link between planting density and growth (P < 0.001). Young trees (1–5 years) grew quite easily because they had plenty of open space. In this young group, the widest rows of 3.66 x 4.57 m gave the best average DBH of 0.09 m. These wide plots recorded a total biomass of 1.33 Mg ha-1 and a CO2 mitigation value of 2.44 Mg ha-1. But things took a tough turn as the trees hit the 6–10 years mature stage. Heavy crowding on these dry Vertic soils started blocking growth in the tightest 3.05 x 3.05 m layout. This resource stress capped the mature DBH at just 0.07 m and dragged the stand biomass down to 2.09 Mg ha-1. In comparison, the medium spacing of 3.66 x 3.66 m hit the perfect sweet spot by keeping individual trees healthy while protecting the farm's overall yield. In the mature stage, this intermediate spacing hit a top average height of 4.79 m, the best DBH of 0.12 m and a great stand volume of 40.43 m3 ha-1. Due to these superior dimensions, the medium plots maximised mature biomass at 4.30 Mg ha-1 and kept a peak net C stock of 2.15 Mg C ha-1. This adds up to a solid climate benefit of 7.89 Mg CO2 ha-1. Since the interaction between age and spacing was non-significant, we can safely say this medium setup stays highly stable over time. In short, these real-world numbers give a reliable roadmap for connecting private sandalwood farming with global climate programs like reducing emissions from deforestation and forest degradation (REDD+).
This study was conducted to identify potential maize inbred lines for hybrid development through morphological and molecular heterotic grouping. A total of 120 test crosses developed using 60 inbred lines and two heterotic testers (LM-13 and LM-14) in a line × tester mating design (rabi, 2019–20) were evaluated for grain yield along with parental lines across 3 locations (Maize Research Center (MRC), Rajendranagar and Agricultural Research Station (ARS) at Karimnagar and Madhira, Telangana, India) during rabi, 2020–21. Molecular diversity among 62 parental lines was assessed using 30 simple sequence repeat (SSR) markers at the Institute of Biotechnology, Rajendranagar, Hyderabad, Telangana, India. Pooled analysis of variance revealed significant differences among genotypes across locations, indicating the presence of ample genetic variability for grain yield. Based on the heterotic group specific and general combining ability (HSGCA) method, the inbred lines were classified into two distinct heterotic groups, while molecular diversity analysis grouped them into 4 clusters, revealing substantial genetic divergence. The integration of both morphological and molecular analyses enabled the identification of 12 promising inbred lines common to both approaches; six lines (MGC-90, PFSR-51, GP-35, GP-111, BML-11 and BML-20) associated with tester LM-13 and six lines (PFSR-151, PFSR-46, MGC-15, MGC-137, PFSR-129 and PFSR-95) associated with tester LM-14. These diverse inbred lines hold potential for generating superior segregants in hybridisation and advancing precision breeding programmes in maize.
