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Enhancing maize yield in a conservation agriculture-based maize (Zea mays)- wheat (Triticum aestivum) system through efficient nitrogen management.
No full textThis study evaluated the impact of contrasting tillage and nitrogen management options on the growth, yield attributes, and yield of maize (Zea mays L.) in a conservation agriculture (CA)-based maize-wheat (Triticum aestivum L.) system. The field experiment was conducted during the rainy (kharif) seasons of 2020 and 2021 at the research farm of ICAR-Indian Agricultural Research Institute (IARI), New Delhi. The experiment was conducted in a split plot design with three tillage practices [conventional tillage with residue (CT), zero tillage with residue (ZT) and permanent beds with residue (PB)] as main plot treatments and in sub-plots five nitrogen management options [Control (without N fertilization), recommended dose of N @150 kg N/ha, Green Seeker GS based application of split applied N, N applied as basal through urea super granules USG+ GS based application and 100% basal application of slow release fertilizer (SRF) @150 kg N/ha] with three replications. Results showed that both tillage and nitrogen management options had a significant impact on maize growth, yield attributes, and yield in both seasons. However, time to anthesis and physiological maturity were not significantly affected. Yield attributes were highest in the permanent beds and zero tillage plots, with similar numbers of grains per cob (486.1 and 468.6). The highest leaf area index
(LAI) at 60 DAP was observed in PB (5.79), followed by ZT(5.68) and the lowest was recorded in CT (5.25) plots.
The highest grain yield (2year mean basis) was recorded with permanent beds plots (5516 kg/ha), while the lowest was observed with conventional tillage (4931 kg/ha). Therefore, the study highlights the importance of CA practices for improving maize growth and yield, and suggests that farmers can achieve better results through the adoption of CA-based permanent beds and use of USG as nitrogen management option
Impact of agro-geotextiles on soil aggregation and organic carbon sequestration under conservation tilled maize-based cropping system in the Indian Himalayas
No full textAlthough agro-geotextile (AGT) emplacement shows potential to mitigate soil loss
and, thus, increase carbon sequestration, comprehensive information is scanty on
the impact of using agro-geotextiles on soil organic carbon (SOC) sequestration,
aggregate-associated C, and soil loss in the foothills of the Indian Himalayan
Region. We evaluated the impacts of Arundo donax AGT in different configurations on SOC sequestration, aggregate stability, and carbon management index (CMI) since 2017 under maize-based cropping systems on a 4% land slope, where eight treatment procedures were adopted. The results revealed that A. donax placement at 0.5-m vertical-interval pea–wheat (M + AD10G0.5-P-W) treatment had ~23% increase in SOC stock (27.87 Mg·ha−1) compared to the maize–wheat (M-W) system in the 0–30-cm soil layer. M + AD10G0.5-P-W and maize–pea–wheat treatments under bench terracing (M-P-W)BT had similar impacts on SOC stocks in that layer after 5 years of cropping. The total SOC values in bulk soils, macroaggregates, and microaggregates were ~24, 20, and 31% higher, respectively, in plots under M + AD10G0.5-P-W treatment than M-W in the topsoil (0–5 cm). The inclusion of post-rainy season vegetable pea in the maize–wheat cropping system, along with AGT application and crop residue management, generated additional biomass and enhanced CMI by ~60% in the plots under M + AD10G0.5-P-W treatment over M-W, although M + AD10G0.5-P-W and (M-P-W)BT had similar effects in the topsoil. In the 5–15-cm layer, there was no significant effect of soil conservation practices on CMI values. Under the M + AD10G0.5-P-W
treatment, the annual mean soil loss decreased by ~92% over M-W treatment. We observed that CMI, proportion of macroaggregates, aggregate-associated C, labile C, total SOC concentration (thus, SOC accumulation rate), and mean annual C input were strongly correlated with the mean annual soil loss from 2017 to 2021. The study revealed that the emplacement of an A. donax mat and incorporation of a legume in a cropping system (M-W), conservation tillage, and crop residue retention not only prevented soil loss but also enhanced C sequestration compared to farmers’ practice (M-W) in the Indian Himalayas. The significance of this study is soil conservation, recycling of residues and weeds, and climate change adaptation
and mitigation, as well as increasing farmers’ income.Department of Science and Technology Government of Indi
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No full textNot AvailableThe agricultural sector in Assam, India, holds immense potential for economic growth and rural development. However, harnessing this potential requires tackling challenges like low productivity, resource scarcity, and climate change. Machine learning (ML) emerges as a promising tool to address these hurdles and transform Assam’s agricultural sector. This review investigates the potential of machine learning (ML) techniques in driving agricultural growth within the specific context of the Assam’s economy. Utilizing comprehensive search within Scopus and Web of Science databases from 2015 to 2023, and following the PRISMA guidelines, we analyzed 37 relevant articles. Our examination focuses on the multifaceted applications of ML across various agricultural domains in Assam, encompassing crop yield prediction, soil health analysis and economic growth. The review highlights successful ML- driven interventions in Assam’s agricultural sector, showcasing their ability to improve resource efficiency, optimize crop management, and enhance market access. This review provides valuable insights for policymakers, researchers, and farmers seeking to leverage the power of ML for a more sustainable and prosperous Assam’s agricultural landscapeNot Availabl
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No full textNot AvailableEarly and accurate determination of pregnancy is critical to optimum reproductive performance in pigs and enables farmers
to early rebreed or cull non-pregnant animals. Most of the conventional diagnostic methods are unsuitable for systematic
application under practical conditions. The advent of real-time ultrasonography has made it possible to establish relatively
more reliable pregnancy diagnosis. The present study was carried out to evaluate the diagnostic accuracy and effectiveness of
trans-abdominal real-time ultrasound (RTU) imaging vis-à-vis pregnancy status in sows reared under intensive management.
Trans-abdominal ultrasonographic examinations were performed using a mechanical sector array transducer and portable
ultrasound system in crossbred sows from 20 days post-insemination for up to next 40 days. Animals were followed up for
subsequent reproductive performance with farrowing data used as the definitive test for deriving predictive values. Accuracy
for diagnosis was determined by diagnostic accuracy measures like sensitivity, specificity, predictive values, and likelihood
ratios. Before 30 days of breeding, RTU imaging had 84.21% sensitivity and 75% specificity. Relatively higher false diagnosis
rates were obtained in animals checked at or before 55 days after AI than in animals checked after 55 days (21.73% versus
9.09%). Negative pregnancy rate was low with 29.16% (7/24) false positives. Overall sensitivity and specificity, using farrowing history as the gold standard, were 94.74% and 70.83% respectively. The sensitivity of testing tended to be slightly
lower in sows with litter size of less than 8 total born piglets, compared to sows with 8 or more piglets. Overall positive
likelihood ratio was 3.25 while negative likelihood ratio was 0.07. The results indicate that pregnancy in swine herds can be
reliably detected earlier in gestation by 30 days post-insemination using trans-abdominal RTU imaging. This non-invasive
technique with portable imaging system can be used as an integral part of reproductive monitoring and sound management
practices for profitable swine production systems.Not Availabl
