Agricultural Research Research Articles

Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.

Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR-Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC-Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475-580 nm, 660-730 nm, 940-970 nm, 1420-1510 nm, 1875-2040 nm, and 2350-2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.

The impacts of film mulching and ridging on N2O emissions, relevant functional genes, and microbial communities in rain-fed potato fields

Rain-fed potato (Solanum tuberosum) fields in drylands significantly contribute to nitrous oxide (N2O) emissions, making them an important focus of agricultural greenhouse gas research. Film mulching and ridging are key agricultural methods in potato cultivation. Investigating the impact of these methods on N2O emissions, nitrifying/denitrifying functional genes, and microbial communities can provide a theoretical basis for soil emission reduction and more sustainable dryland agriculture. We examine the effects of flat tillage with mulching, ridge tillage with mulching, flat tillage without mulching, and ridge tillage without mulching, on potato fields under natural rainfall conditions in Wuchuan County, China. N2O emission fluxes were monitored using a static (dark) chamber and gas chromatography. Real-time quantitative PCR (q-PCR) was used to quantify abundances of nitrifying and denitrifying bacteria related to N2O emissions at various potato-growth stages. Illumina high-throughput sequencing was used to investigate microbial community structure by targeting 16S rRNA genes; related soil elements (soil temperatures and moisture) are analyzed. Mulching and ridging indirectly influence N2O emissions, nitrifying/denitrifying functional gene copy numbers, and microbial community structure by altering soil temperature and moisture. Cumulative N2O emissions and emission intensity were both consistently higher in ridge tillage with mulching during the potato-growing period. Ammonia-oxidizing archaea are the main microorganisms that control N2O emissions, with nitrification-coupled denitrification also being an important mechanism contributing to high N2O emissions during soil dry–wet cycles. Increased soil temperature and moisture elevated N2O emissions and functional gene copy numbers. The combination of mulching and ridging effectively uses the characteristics of both practices, making Nitrospira the dominant genus, and significantly increases N2O emissions.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

Estimating Stockout Costs and Optimal Stockout Rates: A Case on the Management of Ugly Produce Inventory

Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily to ensure they will maintain the same level of utility they would have obtained had stockouts not occurred. This paper presents an analysis of these compensation costs as applied to the design of optimal stockout rates by an online retailer marketing to consumers aesthetically substandard fruits and vegetables rejected by mainstream grocery chains. Because growers face high uncertainty in their harvesting conditions and in the aesthetic quality of their crops and there are little data on hand to predict the value consumers attach to the availability of subpar produce, it is difficult to optimally match the supply of these products with consumers’ demand. Our analysis draws from a multiple discrete-continuous extreme value (MDCEV) choice model to calculate consumer compensations that the retailer can use to estimate the opportunity costs of stockouts to manage its inventory. We show that not taking into account these compensation costs could unduly inflate the optimal stockout rates for these products. Armed with these compensation cost estimates, we show how these costs can serve as incentives for retailers to source greater inventory amounts of imperfect produce from growers and how this will ultimately translate into less waste in the supply chain. This paper was accepted by Charles Corbett, operations management. Funding: Funding from Agriculture and Food Research Initiative (National Institute for Food and Agriculture, USDA) [Grant 2016-09900] is gratefully acknowledged. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03174 .

Field evaluation of UASD Bt-cotton Event-78 based early segregating generations for cotton leaf hopper

UASD Bt cotton Event-78 based early segregating generations (F2 & F3) derived from three diverse crosses were evaluated for field incidence of cotton leaf hopper during kharif-2021 & 2022 at Agricultural Research Station, UAS, Dharwad. A total 600 F2 plants from the crosses viz., UASD Bt-78 х DHS-29, UASDBt-78 х Suvin and Suvin х UASD Bt-78 were screened of which 103,100 and 102 plants respective to the three crosses were found to be resistant. The segregation of F2 population for the jassid tolerance deciphered the implicit “Inhibitory epistasis” mechanism of host plant resistance. Further evaluation of F3 families of these crosses leads to identification of 103 resistant lines from all the three crosses, the mean leafhopper population ranges from 0.67- 2.67 leafhoppers / 3 leaves with the LHRI score of 1.

Monitoring cropland LAI using Gaussian Process Regression and sentinel – 2 surface reflectance data in Google Earth Engine

ABSTRACT Assessing Leaf Area Index (LAI) is one of the key indicators to study and understand cropland’s productivity. To this end, Sentinel-2 (S2) ideally serves to monitor LAI given its high spatial and temporal resolution. Thanks to the cloud computing prowess of the revolutionary Google Earth Engine (GEE), assessing the temporal changes in LAI over a large area can be achieved quickly, making crop health monitoring in near real-time plausible. To achieve this, this work has integrated the Gaussian Process Regression (GPR) algorithm and the PROSAIL model into GEE to map LAI values for the crops being cultivated accurately. The Automated Radiative Transfer Mode Operator (ARTMO) software generated PROSAIL simulation spectra. The GPR model was trained using the PROSAIL simulation spectra and optimized through the Euclidean distance-Based Diversity (EBD) Active Learning (AL) method. The final GPR model was validated against in-situ data collected on 23rd February, 2023 from the research farms of the Indian Agricultural Research Institute (IARI), New Delhi, India. Through ARTMO, the validated GPR model produced an estimated LAI map over croplands managed by IARI, with an accuracy of 0.688 R2 value, and these LAI values and a precision of 0.96 R2 were observed between the LAI values observed in the ARTMO LAI map and the GEE-generated map. This model was then run into GEE to study the temporal changes in LAI values for Mustard, Chickpea, and Wheat, three prominent crops for the Rabi season. These temporal LAI patterns can assist in understanding the cropland’s phenological changes. Thanks to GEE, crop phenology monitoring can be efficiently realized using temporal LAI estimates.

Effect of Integrated Nutrient Management in China Aster cv. Arka Poornima Under Mid Hills of Himalayas

The present investigation was conducted to investigate the effect of integrated nutrient management on vegetative parameters of China aster cv. Arka Poornima under the mid hills of Himachal Pradesh. The experiment was laid out in simple randomized design (RBD) with eighteen treatments. The experiment was conducted at the Kakhali research farm of Dr. Khem Singh Gill Akal College of Agriculture, Eternal University, Baru Sahib, Himachal Pradesh during kharif season in 2023. For integrated nutrient management eighteen treatments were constituted with three levels of recommend dose of inorganic fertilizers (50, 75 and 100 %) and three biofertilizers viz., Azotobacter, PSB and KSB along with control. The observations for the vegetative parameters plant height (cm) at maturity, numbers of leaves, number of primary branches, number of secondary branches, leaf area per plant, leaf area index and plant spread were recorded and analyzed. The plant height (91.40 cm), No. of leaves (356.13), No. of primary branches (12.40) and secondary branches (33.70) were found maximum in treatment T12 (RDF@75% + Azotobacter + PSB + KSB), while the lowest values were observed in T0 (control). The highest leaf area (27.49 cm2) was found in T8 (RDF@75% + Azotobacter), while the leaf area index (12.02) and plant spread (16.57 cm) were found in treatment T11 (RDF@75% + Azotobacter + PSB). The lowest values for leaf area (17.77 cm2), leaf area index (5.48) and plant spread (11.82 cm) were found in treatments T14 (RDF@100% + Azotobacter), T16 (RDF@100% + KSB) and T10 (RDF@75% + KSB), respectively. Therefore, it can be concluded that, application of T12 (RDF@75% + Azotobacter + PSB + KSB) is best for the getting higher production in China aster followed by T11 (RDF@75% + Azotobacter + PSB). This study will reduce the use of inorganic fertilizer without polluting the soil and environment.

Correlation and Path Analysis in Cowpea (Vigna unguiculata (L.) Walp.)

This study, conducted at Horticultural Research Farm of Babasaheb Bhimrao Ambedkar University, Lucknow, India, over 2020-21 and 2021-22, aimed to examine coefficient of correlation and direct and indirect effects of yield contributing traits on economic yield among 30 diverse cowpea genotypes. The research employed a Randomized Block Design (R.B.D.) with three replications, assessing twenty-six quantitative traits. Genotypic correlations exceeded phenotypic correlations, underscoring genetic influence over environmental factors. Key findings included plant height's positive correlation with branches per plant and days to first flowering, while negatively correlating with nodes on main branches and pod diameter. Traits such as number of pods per plant and average pod weight showed strong positive correlations with pod yield, highlighting their importance in breeding programs. Path coefficient analysis revealed significant positive direct effects on pod yield per plant from traits including plant height, branches per plant, and average pod weight. Indirect effects through traits like days to first flowering and non-reducing sugars also contributed to pod yield. These insights into trait correlations and effects are crucial for developing superior cowpea genotypes with enhanced yield and agronomic traits. The findings emphasize the importance of genetic variability in breeding programs, enabling the selection of superior genotypes to improve cowpea productivity.

Tillage and Seaweed Extract Effects on Soil Physio-chemical Properties, Microbial Population and Yield of Sweet Corn (Zea mays saccharata L.) in Hills of Nagaland

Background: Sweet corn is a selectively crossbred variant of maize designed with the intention of enhancing its sugar content. It highly remunerative and is gaining momentum among the progressive farmers due to its extra sweetness, early maturity, nutritious green fodder at harvest which earns high market value. Methods: Field experiments were undertaken during kharif season of 2021 and 2022 at Experimental Research Farm, Department of Agronomy, SAS, Nagaland University to access tillage and seaweed extract effects on soil physio-chemical properties, microbial population and yield of sweet corn. Result: The results from study revealed that yield parameters such as green cob yield, green fodder yield showed significant increase under conventional tillage and application of 10% S-SWE. Highest microbial population was exhibited under zero tilled plots, Sargassum seaweed extract (15% S-SWE) application noted significantly higher bacterial, actinomycetes and total microbial population.

Hyposense: An Integrated Sensor Device for Hydroponics Farm Monitoring

In recent years, the monitoring of hydroponics farms has undergone a significant transformation due to the integration of sensors. However, the conventional method of manually observing measurements from multiple sensors has proven to be excessively time-consuming and costly. Additionally, the lack of automatic data recording options in most sensors has been a limiting factor. To address these challenges, we conducted an experimental study to introduce a novel integrated sensor device named “HypoSense”, designed to monitor essential parameters such as temperature, humidity, pH, light intensity, Total Dissolved Solids (TDS), and Electrical Conductivity (EC) in hydroponic systems. HypoSense consists of three microcontroller units (MCUs) incorporated with two controlling circuits based on the Arduino platform: the Arduino UNO, ESP32, and the ESP8266 (NodeMCU) microcontrollers. The device is designed using high-precision sensors, including the DHT11 for temperature and humidity monitoring, an Analog pH sensor kit for pH measurement, BH1750 for light intensity, and an Analog TDS sensor kit for TDS and electrical conductivity measurements. These sensors were chosen for their reliability, accuracy, and compatibility with the Arduino platform, ensuring that HypoSense delivers precise and consistent readings. HypoSense is a cost-effective, portable sensor device specially designed for monitoring growth parameters simultaneously in hydroponic farming, saving growers time and effort. The evaluation was carried out in two phases. Firstly, field sensors were employed to calibrate HypoSense device sensors. Next, a secondary evaluation was conducted to confirm the practicality and user-friendliness of the HypoSense device. To facilitate the evaluation of HypoSense, we set up an indoor hydroponic system for growing tomatoes and lettuce. The results of these evaluations, focusing on the performance and applicability of the HypoSense device, will provide valuable insights for hydroponic growers and future researchers in precision agriculture.

Release of ‘Kikatiti’ a multiple disease resistant pinto bean cultivar with superior productivity in Tanzania identified from evaluation of the Durango Diversity Panel

Abstract‘Kikatiti’ pinto bean (Phaseolus vulgaris L.) (Reg. no. CV‐361, PI 705149) was developed by the dry bean breeding program at the University of Nebraska, Agricultural Research Division, and submitted for release in 2021 by the Sokoine University of Agriculture in Morogoro as an upright indeterminate pinto bean with high yield potential and multiple disease resistance across bean production environments in Tanzania. Kikatiti was officially released in 2024 by the National Seed Committee. Kikatiti is an F7:8 derived line developed using the pedigree breeding method in Nebraska. The exceptional performance of Kikatiti in Tanzania was first identified by evaluation of the Durango Diversity Panel (DDP), which consisted of 200 accessions in on‐station trials conducted in Arusha and Mbeya. Kikatiti was identified as accession DDP‐094 (advanced breeding line NE2‐09‐3). Kikatiti yielded 1584 kg ha−1 across 5 years of trials in Tanzania and matured in 89 days. Kikatiti is resistant to bean rust conferred by the Ur‐3 and Ur‐11 genes, resistant to all strains of Bean common mosaic virus and Bean common mosaic necrosis virus conferred by the I and bc‐3 genes, moderately resistant to common bacterial blight conferred by the SAP6 quantitative trait locus, avoids white mold disease due to its upright architecture, and is moderately susceptible to angular leaf spot disease. Kikatiti has large seed size (35.9 g 100 seeds−1), and in on‐farm trials, farmers ranked Kikatiti above local pinto cultivars for its performance and market potential. Kikatiti has the potential to expand pinto bean production across East Africa.

Measuring Agriculture Research and Development Return on Investment: Relevance for Florida

Measuring Agriculture Research and Development Return on Investment: Relevance for Florida

Registration of R18‐14147: A high‐protein conventional soybean germplasm line

AbstractR18‐14147 (Reg. no. GP‐528, PI 705143) is a high‐protein conventional soybean [Glycine max (L.) Merr.] germplasm of maturity group Mid‐IV developed and released by the University of Arkansas System – Division of Agriculture Research & Extension in 2023. The germplasm is an F3:4 selection from the cross LG10‐3671‐1 × R09‐430. Plants of R18‐14147 have an indeterminate growth habit with purple flowers, gray pubescence, brown pod wall, and imperfect black hilum. Across 28 environments in Arkansas (2019–2022), R18‐14147 yield averaged 4482 kg ha−1, with broad adaptability and yield stability. Seed of R18‐14147 contains on average 429 g kg−1 and 191 g kg−1 of protein and oil on a dry weight basis, respectively, and seed weight is 15.2 g 100 seeds−1. The average seed protein content of this line is 8%–27% higher than the average protein content of the check cultivars used in various trials to evaluate R18‐14147. Additionally, R18‐14147 has been identified as resistant to stem canker. R18‐14147 presents soybean growers in the mid‐southern United States with a promising germplasm alternative combining high yield potential and elevated seed protein content for alternative crop rotation in herbicide‐tolerant cultivar systems, offering potentially seed‐saving cost benefits and serving as a valuable germplasm for new cultivar development.

Molecular Assessment of Genetic Diversity and Bottleneck in Hair Goat Reared in Türkiye

Objective: Being the most preferred and geographically distributed in Türkiye, the Hair goat was screened at a molecular level to evaluate genetic diversity and population structure via microsatellite DNA markers. This paper also aimed to investigate the effects of genetic bottleneck to evaluate whether the Hair goat has maintained its effective population size in recent past. Material and Methods: A total of 411 Hair goats were sampled from farms participating in the "Hair Goat Breeding" project, initiated by the General Directorate of Agricultural Research and Policies in Aydın and Denizli provinces. Sampled animals were genotyped with 18 microsatellite loci to assess genetic diversity, population structure, and genetic bottleneck. Results: A total of 341 different alleles were observed across 18 microsatellite loci in which the highest number of alleles (26) and effective alleles (10.18) were detected in INRA005 and HSC loci, respectively. The average observed heterozygosity (0.73) was lower than the expected value (0.83), whereas all loci turned out to be highly informative (PIC>0.50). Factorial correspondence analysis separated animals into two groups, while a genetic admixture was detected between these groups. STRUCTURE analysis, on the other hand, confirmed that 411 animals were derived from three ancestral populations in which the third group is drawn due to admixed individuals. The Wilcoxon test and mode-shift indicator detected a lack of genetic bottleneck indicating that Hair goats reared in Türkiye have maintained their effective population size in recent past. Conclusion: This study validates that used microsatellite markers are highly polymorphic and could be utilized for revealing genetic diversity in different local goat breeds. The findings recovered in this study could be integrated into breeding and conservation programs, while further studies should adopt SNP array technologies and next-generation sequencing platforms to reveal deeper knowledge about the genetic diversity and population structure of Anatolian goat breeds.

MINIstock: Model for INsect Inclusion in sustainable agriculture: USDA-ARS's research approach to advancing insect meal development and inclusion in animal diets.

Animal agriculture is under pressure to increase efficiency, sustainability, and innovation to meet the demands of a rising global population while decreasing adverse environmental effects. Feed cost and availability are 2 of the biggest hurdles to sustainable production. Current diets depend on sources of grain and animal byproduct protein for essential amino acids which have limited sustainability. Insects have arisen as an attractive, sustainable alternative protein source for animal diets due to their favorable nutrient composition, low space and water requirements, and natural role in animal diets. Additionally, insects are capable of bioremediating waste streams including agricultural and food waste, manure, and plastics helping to increase their sustainability. The insect rearing industry has grown rapidly in recent years and shows great economic potential. However, state-of-the-art research is urgently needed to overcome barriers to adoption in commercial animal diets such as regulatory restrictions, production scale issues, and food safety concerns. To address this need, the USDA Agricultural Research Service "MINIstoc: Model for INsect Inclusion" project was created to bring together diverse scientists from across the world to synergistically advance insect meal production and inclusion in animal diets. Here, we provide a short review of insects as feed while describing the MINIstock project which serves as the inspiration for the Journal of Economic Entomology Special Collection "Insects as feed: sustainable solutions for food waste and animal production practices."

Edible coating functionalized with ornamental plant extracts affect the postharvest quality of guava (Psidium guajava) during storage

Present study was carried out during 2021–23 at ICAR-Indian Agricultural Research Institute, New Delhi to investigate the efficacy of active edible coatings, incorporated with extracts from rose leaves and marigold petals in preserving the quality of mature green guava (Psidium guajava L.) cv. Allahabad Safeda during storage at both ambient (25±2°C) and cold (10±1°C) conditions. The coatings, particularly carboxy methyl cellulose combined with rose leaf extract (CMC+RL) demonstrated the highest effectiveness in minimizing per cent firmness reduction (PFR; 77.02% and 84.71%) compared to the control (89.95% and 89.73%) after 4 and 18 days of ambient and cold storage, respectively. Additionally, the coatings significantly influenced total soluble solids (TSS), total sugar content (TSC) and yellowness index (YI) showing potential in slowing down the fruit ripening. Moreover, the coatings demonstrated notable antioxidant activity with CMC+RL coating exhibited the highest activity. Furthermore, the coatings exhibited a substantial inhibitory effect on polygalactouronase (PG) activity, indicating a potential to mitigate cell wall degradation. Overall, the results highlight the promise of these biodegradable coatings as an alternative to synthetic preservatives in preserving postharvest quality and enhancing the shelf-life of guava fruits.

Integrated Management of Stem and Root Rot of Cowpea Caused by Macrophomina phaseolina (Tassi.) Goid. using Fungicides, Bioagents and Organic Manures

Background: Cowpea crop is affected by various biotic and abiotic stresses which are responsible for its poor quality and low yield resulting in severe economic losses. Among the root diseases, stem and root rot caused by Macrophomina phaseolina is an important disease causing the yield losses ranging from 50-55 per cent. So, there is a need to formulate suitable management practices against root rot. Methods: Field experiment was laid-out in a randomized complete block design with three replications at Main Agricultural Research Station, University of Agricultural Sciences, Dharwad during rabi 2021-22 and 2022-23 to determine the efficacy of economically viable and effective fungicides, bioagents and organic manures against stem and root rot of cowpea. The per cent disease incidence and yield per hectare were taken into consideration for statistical analysis. Result: In laboratory experiments, it was found that, the seed dressing fungicides mancozeb 50% + carbendazim 25% WP and carboxin 37.5% + thiram 37.5% DS were the most effective against M. phaseolina. Similarly, among the bioagents tested, T. harzianum was the most effective followed by T. Viride and P. fluorescens. A two-years evaluation of nine integrated treatment modules for rabi seasons revealed that, seed treatment with carboxin 37.5% + thiram 37.5 % WS resulted in the lowest disease incidence and the highest grain yield, 100 seed weight and B:C ratio. Cowpea stem and root rot incidence was increased with soil temperature and decreased with soil moisture.

Productivity, nutrient uptake and nitrogen economy of wheat (Triticum aestivum) in a conservation agriculture-based cotton (Gossypium herbaceum)–wheat system

Conservation agriculture (CA) practices like zero-till and residue retention along with nitrogen management is vital for improving degraded soil health and optimizing the yield potential of wheat (Triticum aestivum L.) crop. Present study was carried out during winter (rabi) seasons of 2021–22 and 2022–23 at ICAR-Indian Agricultural Research Institute, New Delhi for optimizing wheat productivity in a conservation agriculture-based cotton (Gossypium herbaceum L.)-wheat system. The experiment was laid out in a randomized complete block design (RCBD) with three replications consisting of combination of 3 different crop establishment methods, viz. permanent broad bed (PBB), permanent narrow bed (PNB) and zero-till flatbed (ZTFB) with residue (R) and without residue along with 2 doses of nitrogen (75N, 100N) and conventional tillage (CT). The CA-based practices showed overall superior response in terms of growth, yield and nutrients uptake of wheat than CT. Among them, the PBBR100N resulted in significantly higher plant height, leaf area index, dry-matter accumulation, crop growth rate, and relative growth rate compared to PNB, PBB, ZTFB and CT. This treatment led to significantly higher grain (16.2–19.4%) and straw (9.6–11.6%) yields than CT. The net returns and benefit cost ratio (23.4% and 27.9%) were also higher than that in CT. Again, in this treatment, N uptake by wheat crop was higher by 85%, P uptake by 53.1%, and K uptake by 40.1%, and available soil N, P and K by 16.1, 25.3 and 43.7%, respectively than CT. Therefore, the study suggests that the adoption of CA-based practices with broad bed in Indo-Gangetic Plains will not only enhance the productivity of wheat but also result in improved soil health by restoring soil fertility.

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

Profitability of Soymilk Processing: Implications for Upscaling among Small Scale Enterprises

The profitability of soymilk processing with implications for scaling up among small-scale enterprises was investigated based on data which emanated from the Institute of Agricultural Research and Training (IAR&T), Moor Plantation, Apata Ibadan, Southwest Nigeria. Soymilk, a plant-based product, has gained popularity as a nutritious and sustainable substitute for dairy milk, prompting interest in its production in a commercialized way. In order to establish profitability and upscaling, an economic analysis of soymilk processed at the Institute was carried out on five (5) treatments using two (2) different methods. The partial budgetary and gross margin analysis were used to evaluate the profitability of the soymilk processed. The research outcome showed that treatment Mch2-3.0Syb-3cps was the best with the least cost. Treatment Mch2-3.0Syb-3cps had a positive net benefit of ₦4,090, an incremental net benefit of ₦435, an incremental cost of ₦915 and a marginal rate of returns of 47.54. This implies that compared to other treatments, the Mch2-3.0Syb-3cps method of soymilk processing would give an additional 45.54 litres of soymilk for every ₦1 spent in processing soymilk, which is the highest compared to other treatments. The findings suggest that soymilk processing holds promise as a profitable business if this treatment is adopted and upscaled.