Different Stages Of Crop Growth Research Articles

Monitoring Insect Pest Complex on Black Gram

The pest spectra, comprising of thirteen species, were observed to attack black gram at different stages of crop growth. The stem fly Ophiomyia phaseoli (Tryon), whitefly, Bemisia tabaci (Gennadius), black aphid Aphis craccivora (Koch), pod bug Riptortus pedestris (F), surface grasshopper, Chrotogonus trachypterous (Blanchard), semilooper, Plusia orichalcea (F), tobacco caterpillar, Spodoptera litura (F), blue butterfly Lampides boeticus (L) and blister beetle Mylabris pustulata (Thunberg) have assumed the status of minor pests whereas, gram pod borer Helicoverpa armigera (Hubner), spotted pod borer Maruca vitrata (Geyer), Bihar hairy caterpillar Spilosoma obliqua (Walker) and leafhopper Empoasca kerri (Pruthi) were recorded as major pests.

Design and Testing of Vehicle-Mounted Crop Growth Monitoring System

The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors.

Assessment of Association Between Observed Weather and Detrended Weather with the Detrended Rice Crop Yield in the Konkan Region of Maharashtra

Maharashtra’s total rice production (1961-2014) strongly increases. This is due to the increasing cultivated area and the influence of improved technology. Whereas, year-to-year fluctuation in production is mainly because of fluctuations in the climate. Hence, only the rice crop yield is taken for the detailed study of climate and agriculture in the selected districts from 1981 to 2014 period. Correlation analysis indicates the influence of climate change on detrend yield. A heavy shower of rain and hail can cause immense damage to a standing crop, ready for harvest (Crop weather calendar 2003, IMD). Thus, yield is the summation of conditions in all successive weeks of the crop. Correlation analysis was carried out to see the impact of observed weather with detrend yield and detrended weather with detrend yield. The Research Work explores the importance of additional Climatic variables, other than Temperature and Rainfall, and their impact on Rice crop yield. There is a significant relationship between the Observed Weather Parameter and De-trended Weather Parameter with the De-trend Yield, at different Growth Stages of Crop. The study brought about the Relationship between Rice Crop Yield and Weather Parameters, with and without Climate Change.

Induction of heat stress tolerance in barley (Hordeum vulgare) through thermopriming

Present study was carried during 2021–22 and 2022–23 at ICAR-Indian Institute Seed Science, Mau, Uttar Pradesh to evaluate the effect of thermopriming on inducing heat stress tolerance in barley (Hordeum vulgare L.) grown under suboptimal conditions. The experimental material consisted of two location specific varieties of barley, DWRB 101 and DWRB 123, with two distinct lots, one of fresh seeds and the other of seeds aged for 1-year duration. Temperature regime between 30 to 45°C with 5°C increment and 6, 12, 24, 36 and 48 h duration, was selected as the treatment combinations along with the control (non-treated seeds). Results showed that the quantum of temperature exposure as well as the duration has significant impact on the germination and seedling growth of barley under lab conditions. The ANOVA and Dunken multiple range test enumerated that, the seeds primed at 30°C for 12 h, has positively improved the germination potential and seedling vigour index when compared to the respective lower and higher durations of exposure and revealed it as the best treatment combination. The subsequent year of evaluation of emergence potential of the treated seeds under an artificially induced high temperature stress revealed that the selected treatment was a good performer in inducing heat stress tolerance in barley, and can be chosen as solution to the high temperature stress induced through increasing atmospheric temperature at different stages of crop growth.

Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020

Typhoons are increasing in inland areas and affecting agriculture under climate change. Assessing the large-scale impact of typhoons on crops is essential for improving agricultural resilience. However, typhoon impacts are typically intricate and uncertain, depending on the different stages of crop growth and the indicators used for assessment. Here, we leveraged satellite data to examine the spatial-temporal patterns of typhoon-induced impacts on maize during the milk stage in Northeast China in 2020. We found that land surface water index (LSWI) showed a significantly higher negative relative change of 161.5% in regions severely affected by typhoons, surpassing that of normalized difference vegetation index (NDVI, 19.7%), near-infrared reflectance of vegetation (NIRV, 31.7%), and enhanced vegetation index (EVI, 25.2%), suggesting the outperformance of LSWI in monitoring the impact of typhoons on maize. Based on LSWI, we revealed the spatial-temporal distribution of typhoon impacts. Specifically, significant impacts on maize growth were observed in central Northeast China, particularly in Heilongjiang (49% of maize fields) and Jilin provinces (53% of maize fields). Furthermore, week-long lagging typhoon impacts on maize growth were generally found but lasted until the end of the maize growing season. We also found the typhoon impacts were partially mitigated by spatial and temporal compensations. Regions with above-average maize growth offset the losses experienced in areas severely affected by typhoons. Additionally, better maize growth in the early stages enhanced its resilience against subsequent typhoon impacts. Despite the mitigating effects on maize production, typhoons may increase the risk of maize mold, which warrants further investigation. Given the projected increase in future climate extremes, this study provides valuable insights into the rapid assessment of typhoon impacts using satellite data and cloud computing and supports decision-making in crop management for sustainable agricultural production.

Response of Matching Degree between Precipitation and Maize Water Requirement to Climate Change in China

The synchronicity of rain and heat in the summer of China’s monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region.

Biodiversity and Succession of Insects of Blackgram (Vigna mungo (L.) Hepper) During rabi in the Western Undulating Zone of Odisha

The field experiment was conducted to study about insect biodiversity and their succession in blackgram crop at the crop research farm, College of Agriculture, OUAT, Bhawanipatna during rabi 21. Twenty seven insects belong to seven orders and twenty two families were recorded during different growth stages of crop in an overlapping manner. Beside the insects twelve numbers of predators, three numbers of parasitoids and two pollinators were also noticed. The highest diversity index and evenness of insects were recorded at 42 SMW (0.902 and 1.000) followed by 50 SMW (0.833 and 0.710) respectively. The diversity of insects was almost uniform because of the evenness of the species. The insect order homoptera and the insect white fly came under superdominant class of dominance during all the growth stages of crop, whereas coleoptera and hemiptera were categorized under sub-dominant class at vegetative stage and flowering to podding stage of crop growth respectively. The insect orders hemiptera, coleoptera, diptera and hymenoptera and the insects i.e., galerucid beetle, jassid, stem fly, aphids and thrips came under recedent class of dominance. The relative abundance and dominant status of insect’s revealed that the maximum numbers of insects were belongs to orders homoptera (75.22%) followed by hemiptera (7.92%), coleoptera (6.30%), diptera (4.62%), hymenoptera (4.05%), orthoptera (0.89%), lepidoptera (0.86%) and odonata (0.09%).Chrysomelid beetles were noticed first during the early vegetative stage of crop followed by stem fly, white fly, leaf webber and semiloopers at the vegetative stage, tobacco caterpillar from late vegetative to pod filling stage, thrips in flowering stage, pod borers, pod bugs, aphids, pentatomid bugs and bihar hairy caterpillar from flowering to crop maturity stage and the pod fly and pod wasp from pod filling to pod maturity stage of the crop. Jassids, grasshoppers and predators like spiders, coccinellids beetles and parasitoids were appeared from mid vegetative stage and continued till fag end of the crop. The peak activities of insects were recorded from flowering to pod maturity stage of the crop.

Sensory characteristics of different stages of safflower (Carthamus tinctorius L.) leaves and leaf powder incorporated products

Safflower leaves from four cultivars during different stages of crop growth (30, 50, 70 and 90 days) wereincorporated to develop two traditional productsi.e.,safflowerleaves(35%)incorporated roti(SFLR) and safflowerleaf powder(15%)incorporated "karampodi" (SLKP). Sensory evaluation was carried out on a 9 point hedonic scale for both the products. Results of sensory evaluation revealed that 35% incorporation of safflower leaves in SFLR and 15% incorporation ofsafflowerleaf powderin SLKP products had high scoresfor overall acceptability and there was not much difference in sensory attributes among the cultivars used in the study. It was observed that safflower leaf powder "karam podi" was liked more in all the sensory parameters as compared to safflower leaves roti. It can also be inferred that fresh leaves from non-spiny varieties such as NARI-6 can be consumed up to 90 days where as spiny varieties of safflower such as Annigeri-1, Manjira and TSF-1 can be well acceptable up to 50 days. The spiny varieties can be consumed up to 70th day also, due to their nutrient composition, but should be incorporated at a level lesser than 35% or can be tested in othersuitable recipes. Spiny varieties ofsafflower leaves during 70 days of crop growth, when used as powder will not have any detrimental effects of sensory properties.

Insect Pest Complex on Cauliflower (Brassica oleracea var botrytis L.) and their Correlation with Weather Parameters in Medziphema, Nagaland

Field experiment was conducted to study the insect pest complex and their incidence on cauliflower during rabi season of 2021-2022 in the Entomological farm, SASRD, Medziphema, Nagaland. Fourteen insect pests were found to attack the crop at different stages of crop growth. Out of these, Green peach aphid, Myzus persicae and Cabbage butterfly, Pieris spp. were considered as major insect pests of cauliflower. The peak population of Green peach aphid (8.74 aphids/leaf) and Cabbage butterfly (4.10 larva /plant) was observed on 17th December and 12th February, respectively. The population of Myzus persicae showed a non-significant negative relation with maximum (r = -0.320) and minimum (r = -0.311) temperature, while positive relation with maximum (r = 0.028) and minimum (r = 0.249) relative humidity as well as rainfall (r = 0.021). The population of Pieris spp. showed a non-significant negative correlation with maximum (r = -0.484) temperature, whereas a significant negative correlation was observed with minimum (r = -0.550) temperature. Correlation of relative humidity with the incidence of Pieris spp. exhibited a non-significant negative correlation with maximum (r = -0.078) and minimum (r = -0.162) relative humidity, while rainfall exhibited a non-significant positive correlation (r = 0.235).

Vegetation descriptors from Sentinel-1 SAR data for crop growth monitoring

Synthetic aperture radar (SAR) remote sensing technology has the advantage of all-weather observation and can acquire time-series images with crop growth period, which has great potential for applications such as crop phenology analysis. However, available studies primarily focus on conducting statistical and crop growth analyses based on the polarization or backscatter intensities of SAR images, and the exploration of polarization scattering information in SAR images is not sufficient. To comprehensively reflect the polarization characteristics and scattering mechanisms of crop at different growth stages, we propose a new method for extracting vegetation descriptors from Sentinel-1 dual-polarimetric SAR data. The method combines the backscattering intensity and polarization decomposition information to construct a normalized index q, which is used to generate three vegetation descriptors: the co-pol purity parameter (mcp), the pseudo-scattering angle (θcp), and the pseudo-scattering entropy (Hcp). Further, a novel unsupervised clustering framework, founded on Hcp and θcp, has been proposed. This framework establishes six zones (named as Z1 to Z6) representing distinct physical scattering mechanisms, and by statistically sampling point data, it can determine the growth stage of the crops For validating the performance of the proposed vegetation descriptors and clustering framework, we conducted a three-year experiment using four crops from two publicly available datasets, namely wheat and canola from the Carman in Canada (Test site-1), corn and soybeans from Iowa in the United States (Test site-2). The experimental results indicate that mcp,θcp, and Hcp exhibit regular changes at different growth stages of crops from planting to maturity, with mcp and θcp gradually decreasing while Hcp gradually increasing. Within the entire phenology window, θcp changes by approximately 42°, while both mcp and θcp varies by about 0.9, and the sampling points shift from the Z2 to the Z5 zone. The vegetation descriptors are highly sensitive to the growth status of crops, and the clustering framework can also effectively respond to different growth stages of vegetation. Furthermore, the vegetation descriptors and clustering framework proposed in this study have the potential for extended application to different crop types and other polarimetric SAR data sources.

Study on Effect of Rainfall Variability on Crop Productivity of Tur (Arhar) and Cotton Crops for Various Districts of Maharashtra, India

This research article investigates the impact of rainfall variability on the productivity of Tur (Arhar) and Cotton crops in the districts of Maharashtra, India. The study analyzes the correlation between rainfall departure (deviation from the normal) and crop productivity, focusing on specific months when rainfall has the most significant influence on crop yield. Secondary data from the India Meteorological Department and the Ministry of Agriculture and Farmers Welfare were utilized for the analysis. Statistical methods such as detrending, correlation analysis, and significance testing were employed to measure the relationship between rainfall variability and crop productivity. For the Tur (Arhar) crop, the study covers data from 1997-1998 to 2018-2019 for 34 districts in Maharashtra. The analysis reveals the effects of rainfall on different stages of crop growth, such as sowing, germination, vegetative growth, flowering, grain formation, and harvesting. Similarly, for the Cotton crop, data from 28 districts were examined to determine the impact of rainfall on various stages of crop development. The findings indicate that rainfall plays a crucial role in crop productivity, with both positive and negative correlations observed in different districts and months. The study highlights the significance of rainfall during specific stages of crop growth and emphasizes the importance of considering rainfall patterns when making agricultural decisions. This research contributes valuable insights into the relationship between rainfall variability and crop productivity, providing farmers and policymakers with knowledge to develop sustainable agricultural strategies in Maharashtra. By understanding the influence of rainfall on crop yield, farmers can make informed decisions regarding crop selection and adopt appropriate agricultural practices to mitigate the effects of rainfall variability.

Applicability of machine learning techniques in predicting wheat yield based on remote sensing and climate data in Pakistan, South Asia

Machine learning (ML) algorithms perform better than classical statistical approaches to explore hidden nonlinear relationships. In this context, the goal of this research is to predict wheat yield utilizing remote sensing and climatic data in southern part of Pakistan. Four remote sensing indices, viz.., Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI) are integrated with five climatic variables, i.e., Maximum Temperature (Tmax), Minimum Temperature (Tmin), Rainfall (R), Relative humidity (RH) and windspeed (WS) and one drought index, i.e., Standardized Precipitation Evapotranspiration Index (SPEI). Eight model combinations are built within two scenarios of wheat season, i.e., Whole Seasonal mean (WSM) (SC1), and Peak of Seasonal Mean (POSM) (SC2). Two nonlinear ML algorithms, i.e., Random Forest (RF), and Support Vector Machines (SVM), and one linear model, i.e., LASSO is being employed for wheat yield prediction to find the best combination and ML algorithm in two scenarios. Results revealed that in SC1, RF regression for the model combination (GNDVI +Tmax+ Tmin + R + RH + WS) outperformed other models (R2 = 0.71, RMSE = 2.365). Similarly, in SC2 RF regression outperformed SVM with model combination (GNDVI + Tmax+ Tmin + R + RH + WS) performed highest with R2 = 0.78, and lowest RMSE = 2.07, followed by (GNDVI + SPEI + RH + WS; R2 = 0.75). Interestingly, linear LASSSO also performed equally with RF with R2 = 0.77–0.73 in both scenarios. However, the output of this research recommends using SC2 for yield prediction in ML models. Overall, this research reveals the significance and potential of ML techniques for timely prediction of crop yield in different stages of crop growth that provide a solid foundation for food security in the region.

Constraints perceived by farmers in adoption of biofertilizer technologies

The present study was conducted during 2021-2022 in Dharwad and Gadag districts of North Karnataka with a specific objective to identify the constraints faced by farmers in adoption of biofertilizer technologies. Sixty farmers each of both districts were identified through simple random sampling method, thus constitutes a total sample of 120 respondents. In the investigation, ex-post facto research design is being employed. The interview schedule was developed based on the objective of the study and review of literature. The information was collected by personal interview method. Constraints and suggestions were analysed using Garrett’s ranking technique and it is observed that lack of required knowledge on biofertilizer technologies ranked first among constraints with garret score 73.00 followed by non-availability of biofertilizers locally at times when needed (71.60), slow results of biofertilizer technologies on different growth stages of crop or on standing crop (65.29), lack of technical guidance about biofertilizer technologies (58.10) and lack of financial support from government (56.00). The important suggestions stated by the farmers for improving adoption of biofertilizer technologies were organization of training/demonstrations on biofertilizer technologies on usage of biofertilizers, need of government support in the form of subsidies on biofertilizers and assured availability of bio-fertilizers in villages.

Effect of herbicides on weed control efficiency and Yield Response of Kharif Cowpea (Vigna unguiculata L.) in sandy loam soils of Punjab

A field experiment using a randomized block design, eight weed management treatments, and three replications was carried out in the kharif of 2022 at the Division of Agriculture, Lovely Professional University, Phagwara, Punjab, India. To determine the economics and effectiveness of weed control measures for managing weeds in cowpea [Vigna unguiculata (L.). The weed control techniques had a big impact on the weed restrictions.With pre-emergence application (PE) of pendimethalin 0.70 kg/ha combined with hand weeding, the weed density and biomass and greater weed control efficiency were consistently inferior at different stages of crop growth. This confirmed that pre-emergence herbicide application could effectively regulate the weeds emerging early in the season, and the actual controlling of weeds by it led to higher yield and B: C ratio.

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system—entitled EGYPADS—based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.

Exogenous regulation of the potatoes’ adaptive potential when using bio stimulants

Potato from the Solanaceae family is one of the most important crops in the world and its cultivation is common in many places. The average yield of this crop is 20 Mg·ha –1 and it is compatible with climatic conditions in many parts of the world. The experiment studied the possibility of exogenous regulation of the adaptive potential available for four potato cultivars through the use of growth stimulants with different action mechanisms: 24-epibrassinolide (EBL) and chitosan biopolymer (CHT). The results allowed us to establish significant differences in growth parameters, plant height, leaf index, vegetation index, chlorophyll content, and yield structure. Monitoring growth and predicting yields well before harvest are essential to effectively managing potato productivity. Studies have confirmed the empirical relationship between the normalised difference vegetation index ( NDVI) and N-tester vegetation index data at various stages of potato growth with yield data. Statistical linear regression models were used to develop an empirical relationship between the NDVI and N-tester data and yield at different stages of crop growth. The equations have a maximum determination coefficient (R 2) of 0.63 for the N-tester and 0.74 for the NDVI during the flowering phase (BBCH 1 65). NDVI and N-tester vegetation index positively correlated with yield data at all growth stages.

A Review on the Management of Country Bean (<em>Lablab purpureus</em> L.) Diseases in Bangladesh

Purpose: Country bean (Lablab purpureus L.) is an important pulse crop consumed as a vegetable in the central and south-western regions of Bangladesh after eggplant and taro. It promises to ameliorate nutritional demand from vegetables and has an excellent possibility for the world market. But the production is hampered due to infection of several diseases in field conditions. Research method: This study was undertaken based on secondary data of existing literature from Bangladesh and other parts of the world. So far, many research works were done on this issue but those were not available to the policy makers, extension workers, and public in a systematic manner to date. Findings: In this paper, we tried to bring forth different aspects of phytopathological problems of country bean. It usually undergoes stresses from different soilborne to seed-borne pathogens and expresses symptoms from the seedling stage to maturity. Crop protection largely depends on the integration of host plants, seeds, agronomic practices, environmental footprints, and the use of appropriate agrochemicals based on the epidemiology of target pathogens. Here, we also described effective management strategies against respective pathogens of the diverse category. These microorganisms attack at different stages of crop growth and can affect the host plants enormously to cause maximum yield loss. Research limitations: The study focused on the management of country bean diseases based on biological and chemical approaches. It presents limited information on specific technologies in different agroecological zones. Originality/Value: This study identified research gaps among Bangladesh and other countries. It also provides information to combat country bean diseases to the economic threshold level for ensuring sustainable crop yield.

Liquid Bioformulation Regulates Soil Microbial and Enzymatic Activities, and Nutrient Dynamics in Drip-Irrigated Aerobic Rice

ABSTRACT The use of liquid formulations―prepared from naturally available farm wastes―has increasingly gained popularity in recent years; however, it remains unclear how these amendments influence the rhizosphere soil biochemistry, particularly in aerobic rice. In this study, we reformulated the traditional liquid manure (Jeevamrutha) with microbial consortia as liquid bioformulation (LBF), and evaluated its potential effect (when applied with or without NPK-fertilizers) on soil biochemical properties and nutrient availability at different stages of crop growth. Six treatments were tested: (i) absolute control, (ii) conventional practice (CP), LBF applied twice at 2000 L ha–1 with (iii) 100% recommended dose of fertilizer (RDF) through a multi-split fertigation (LBF+NPK100), (iv) 75% RDF (LBF+NPK75), (v) 50% RDF (LBF+NPK50), and (vi) LBF alone. The results showed that soil microbial biomass carbon, total bacterial and fungal load, dehydrogenase, urease, and acid phosphatase activities were significantly (p < .05) higher in the LBF co-applied plots, particularly at the panicle initiation and maturity stages of rice. However, actinomycetes and alkaline phosphatase showed an irregular trend with the growth stages. The rhizosphere soil microbial index (RSMI) values were significantly higher under LBF co-applied treatments over the CP, signifying the improvement of soil quality. Grain yield with LBF+NPK75 increased by 30.3−37.8% and 19.3−32.4% for the summer and rainy seasons, respectively, compared with the CP. Combining LBF with NPK-fertigation had more long-lasting effect on soil N and P availability. Therefore, our findings suggest that substituting about half of RDF with LBF could bring promising agronomic and soil ecological benefits to the rice production under aerobic conditions.

Can 2,4-D promote the hormesis effect in upland rice?

This study aimed to evaluate the influence of low doses of 2,4-D on the agronomic traits of upland rice applied at different stages of crop growth. The work was carried out in a randomized completly blocks, and consisted of the application of 5 low doses of the 2,4-D herbicide (0, 0.68, 1.36, 2.04, 3.40 and 5.44 g acid equivalent (e.a.) ha−1) in two stages of rice development (tillering and floral differentiation). Nitrogen contentes in leaves, SPAD index and yield are higher when low doses of 2,4-D is applied in the tillering stage. Application of 2,4-D at a dose of 2.04 g a.e ha−1 results in a 19% increase in the number of spikelet per panicle. On the other hand, there is no effect of the application of low doses of 2,4-D on height, number of stems, active tillering and weight of 100 seeds. Our results contribute to increase knowledge of the hormesis effect in plants in order to increase crop yield.

DUS characterization of traditional varieties and advanced cultivars of rice using 29 essential characters

For the establishment of the distinctness, forty-four genotypes of rice were characterized at different stages of crop growth using 29 agro-morphological traits following Distinctiveness, Uniformity and Stability test (DUS) during rabi, 2020-21 at Regional Agricultural Research Station, Warangal. The rice genotypes under study recorded a wide range of variability for most of the essential traits studied. Out of 29 descriptors studied, two characteristics leaf auricles and leaf ligule shapewere found monomorphic, six were dimorphic, ten traitswere trimorphic, seven other characters resulted tetramorphic. The descriptors that registered maximum variation were decorticated grain: colour, decorticated grain: shape (in lateral view), panicle: attitude of branching, spikelet: colour of tip of lemma were found to be polymorphic in nature. This comprehensive characterization of rice genotypes is very important for rice breeding and will be beneficial for breeders, researchers and farmers from the stand point of selection and conservation of different landraces for further utilization in crop improvement programmes and also to seek protection under Protection of Plant Varieties and Farmers Rights Act.