Families of Multivalent Analytic Functions Associated with the Convolution Structure

The main aim of the present paper is to introduce a new class of multivalent analytic functions by using the familiar concept’s of convolution structure. The results investigated in the present paper include the characterization properties for this class of analytic functions. Some new and interesting consequences of our results are also pointed out

ON THE INVERSE LAPLACE TRANSFORM OF H -FUNCTION ASSOCIATED WITH FEYNMAN TYPES INTEGRALS

Abstract. The Laplace transform and its inverse are fundamental and powerful tools in solving boundary value problems occurring in the diverse fields of engineering. Here we will establish some useful formulas giving the inverse Laplace transform of various products of algebraic powers and H -function, involving one and more variables, which are unified and likely to have applications in several different areas

Rice yield gaps and nitrogen-use efficiency in the Northwestern Indo-Gangetic Plains of India: Evidence based insights from heterogeneous farmers’ practices

A large database of individual farmer field data (n = 4,107) for rice production in the Northwestern Indo-Gangetic Plains of India was used to decompose rice yield gaps and to investigate the scope to reduce nitrogen (N) inputs without compromising yields. Stochastic frontier analysis was used to disentangle efficiency and resource yield gaps, whereas data on rice yield potential in the region were retrieved from the Global Yield Gap Atlas to estimate the technology yield gap. Rice yield gaps were small (ca. 2.7 t ha−1, or 20% of potential yield, Yp) and mostly attributed to the technology yield gap (ca. 1.8 t ha−1, or ca. 15% of Yp). Efficiency and resource yield gaps were negligible (less than 5% of Yp in most districts). Small yield gaps were associated with high input use, particularly irrigation water and N, for which small yield responses were observed. N partial factor productivity (PFP-N) was 45–50 kg grain kg−1 N for fields with efficient N management and approximately 20% lower for the fields with inefficient N management. Improving PFP-N appears to be best achieved through better matching of N rates to the variety types cultivated and by adjusting the amount of urea applied in the 3rd split in correspondance with the amount of diammonium-phosphate applied earlier in the season. Future studies should assess the potential to reduce irrigation water without compromising rice yield and to broaden the assessment presented here to other indicators and at the cropping systems level

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R2, root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R2 ranging between 367 and 470 kg ha−1, 276–345 kg ha−1, and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha−1 for nitrogen application rate to 372 kg ha−1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha−1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data

Convex and starlike criteria

We investigate an expression involving the quotient of the analytic representations of convex and starlike functions. Sufficient conditions are found for functions to be starlike of a positive order and convex

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Not AvailableThe rising economic and environmental costs of mineral fertilizers associated with lower nutrient use efficiency, and the need to respond the limitations of N fertilization under residue retained condition of conservation agriculture (CA) motivate the research for alternative N placement methods. The third principle of CA, i.e., residue retention on the soil surface hinders the right placement of split applied nitrogen (N). To address this issue, we assessed the impact of three N placement methods, i.e., NPM1: both the N splits were surface band placed, NPM2: the first split of N was sub-surface point placed and second N split (late vegetative stage) was surface band applied, and NPM3: both the N splits were sub-surface point placed, under 4-long-term tillage and residue management ( + R) options, i.e., permanent raised bed (PB + R), zero-till flat (ZT + R) conventional till flat (CT + R) and first time zero till flat sowing of the crop on last 10-year fallow land (FZT + R), in an on-going longterm study (since 2008) in maize for three consecutive years (2018–2020). Results showed that sub-surface point placement of both the N splits (NPM3) increased maize grain yield by 4.7, 7.0 and 6.0% (3-years mean basis) compared to NPM2, under CA-based PB, ZT, and FZT plots, respectively. The peak growth rate in the CA-based PB + R plot was advanced by 4-days with a 9.2% higher growth rate compared to CT + R. Similarly, the peak growth rate in NPM3 was 20% higher than NPM1 plots. The changes in soil properties under CA altered the crop growth behavior, while sub-surface point placement of split applied nitrogen (N) increased the grain N content and altered the peak growth rate of maize. The variability in maize grain yield was best described by cob length and number of cobs in long-term tillage and by cob length in N management plots. The cob length and grains per cob were increased by 4.8–8.7 and 8.6–12.8% under CA-based plots compared to CT + R, respectively. The amount of vegetative stage accumulated N remobilized to maize grain was 21.2% higher under PB + R compared to CT + R plots, while the N remobilization in NPM3 was 22.9% higher compared to NPM1 plots. Similarly, the contribution of reproductive stage N uptake to grain was 9–12% higher in CA-NPM3 compared to CT-NPM1 plots. Further, the early and vigorous growth of maize resulted in a higher accumulation of N and its remobilization to the grains in CA-based and N point placed plots. The sub-surface point placement of N (NPM3) resulted in a 12.8, 14.5 and 9.2% higher benefit-cost ratio compared to NPM1 plots in 11th (2018), 12th (2019) and 13th (2020) years of experimentation, respectively. Therefore, the present study visualizes the impact of a decade-long CA and efficient N management on crop growth behavior, N uptake and remobilization and crop productivity and water use efficiency. This study provides evidence to popularize this technology in the CA-systems of IndoGangetic Plains and other similar agro-ecologies.Not Availabl

Water budgeting in conservation agriculture-based sub-surface drip irrigation using HYDRUS-2D in rice under annual rotation with wheat in Western Indo-Gangetic Plains

Rapidly depleting groundwater in western Indo-Gangetic Plains (IGP) is a major threat to food security in South Asia. Conventional tillage-based and flood irrigated puddled transplanted rice (PTR) is a major contributor to faster depleting aquifers. Urgent actions are therefore warranted to develop alternate productive, profitable, water and N-use efficient rice production practices for rice-wheat (RW) cropping system. Conservation agriculture (CA) based direct-seeded rice (DSR) has been advocated as a potential alternative to PTR. Further, bundling CA with precision water and N management using sub-surface drip irrigation (SSD) has demonstrated significant benefits over CA-based flood irrigation (FI). However, for more efficient use of water, water budgeting is needed which is a challenging task as it requires expensive tools, and time, and efforts. Information about complete water budgeting in high water demanding crops like rice grown under CA-based SSD, FI, and PTR are not available. We deployed HYDRUS-2D model for estimating water budgeting of rice under CA+ (CA-based SSD), CA-based FI, and PTR-based systems. The objective of our study was to calibrate and validate the HYDRUS-2D model to simulate water dynamics in rice grown under CA-based SSD and FI compared to PTR and to design water and N- use efficient production practices for rice cultivation in western IGP. Five treatments comprised of PTR+FI with 120 kg N ha−1 (PTR), zero-till direct-seeded rice (ZTDSR)+FI without N (ZT-N0), ZTDSR+FI with 100% of N recommended dose (ZT-N100), ZTDSR+SSD without N (SSD-N0), and ZTDSR+SSD with 100% of N-recommended dose (SSD-N100) were compared. The result showed that the HYDRUS-2D model satisfactorily simulated the soil moisture content with low root mean square error (RMSE) (0.014–0.028), high coefficient of determination (74–92%), and model efficiency (59–87%) during the simulation period (80 days: 35–114 days after sowing). The highest grain yield (7.18 t ha−1) was observed in the PTR treatment, which was statistically similar to SSD-N100 (6.54 t ha−1) and significantly higher than ZT-N100. During the simulation period, PTR plots received 131.7 cm of water (rainfall + irrigation) which was 27.3% and 50.1% higher than ZT-N100 and SSD-N100 plots, respectively. Out of the cumulative water applied, PTR transpired only 18.4% of applied water, compared to 24% in ZT-N100 and 36.3% in SSD-N100. Interestingly, SSD-N100 plots recorded 20.6% and 23.5% less evaporative loss and 45.0% and 66.0% less water loss by deep drainage than ZT-N100 and PTR, respectively. Thus, conversion to CA+ system with 100% N-recommended dose saved 50.1% and 31.3% of water, and consequently attained 2.0 and 1.45-times higher biomass water use efficiency than PTR and ZT-N100, respectively. Based on the results, CA-based SSD could be recommended for precise utilization of water and to curtails the unproductive water loss components such as evaporation and deep drainage

Co-implementation of precision nutrient management in long-term conservation agriculture-based systems: A step towards sustainable energy-water-food nexus

The conventionally managed cereal-based cropping systems in the Indo-Gangetic Plains (IGP) of South Asia are energy intensive that overwhelm the farm profits and the environmental footprint. This research addresses a complex nexus between yield-energy-water-GHG footprints-economics of conservation agriculture (CA)-based intensified maize-wheat-mungbean rotation. This study evaluated the effect of long-term CA (2012–2020) with optimum nutrient management (2017–20) on energy budgeting, productivity, water and C-footprints, Water productivity (WP), and economics of the CA-based maize-wheat-mungbean system. CA-based permanent bed- and zero tillage flatbed with preceding crop residue retention were compared with the conventional till with preceding crop residue incorporation. These treatments were factored over three-nutrient management alternatives, i.e., GreenSeeker®-guided-N, site-specific nutrient management (SSNM), and recommended fertilizers' dose (Ad-hoc), were compared with farmers' fertilizers practices (FFP). Permanent bed and zero tillage treatments registered higher systems' productivity (18.2 and 12.0%), net returns (44.7 and 34.7%) and water productivity (35.6% and 22.1%), and C-sequestration (54.8 and 62.3%), respectively, over conventional till. Permanent bed- and zero tillage treatments increased the systems' net energy (NE), energy use efficiency (EUE), energy productivity (EP), and energy intensity (EI) by 22.6 and 14.0; 10.1 and 5.6; 9.7 and 5.4; 28.3 and 24.0%, respectively, over conventional till. Conventional till recorded higher net CO2-eq emission (26.5 and 27.2%), C-footprint (20.8 and 14.5%), and water footprint (27.3 and 18.0%) than permanent bed- and zero tillage treatments. SSNM increased the system's productivity, water productivity, and energy use efficiency, while reducing the system's water- and C-footprints and net CO2-eq emission. Thus, adopting permanent beds as a crop establishment method with SSNM could be a feasible alternative to attain higher productivity, profitability, and resource use efficiency in the maize-wheat-mungbean system in northwest India