Yield Trends Research Articles

Effect of methanol on the pyrolysis behaviour of kerogen by ReaxFF molecular dynamics simulations

ABSTRACTA series of molecular dynamics simulations were performed using the ReaxFF reactive force field to investigate the pyrolysis of Longkou oil shale kerogen in the presence of methanol. The roles of methanol in kerogen pyrolysis were deeply explored to reveal the microscopic mechanism of methanolysis by analyzing the distribution of products, the consumption of methanol, and the formation processes of typical products at different temperatures. The results demonstrate that the yield of shale oil in the presence of methanol is consistently higher than that of kerogen direct pyrolysis at the same temperature, which is consistent with the trend of oil yield with temperature reported by previous methanolysis experiments. Methanol supplies a large number of small radicals under the pyrolysis condition, which can facilitate the breakage of weak bonds and take part in the formation of products through combining with the radical fragments generated by the pyrolysis of kerogen. This study will provide theoretical guidance for understanding the methanolysis process of oil shale.

Sustaining Soil Fertility: Exploring the Long-Term Effects of Chemical Fertilizers and Farmyard Manure Application

Long-term fertilizer and manure experiments serve as invaluable resources for investigating various aspects of agriculture and soil management. These experiments provide a wealth of information on yield trends, nutrient dynamics, and soil nutrient balances. They also help in predicting a soil’s carrying capacity, assessing soil quality, and evaluating the sustainability of agricultural systems. Long-term fertilizer experiments play a crucial role in separating the complex interactions among soils, plants, climate, and management practices and shedding light on their impacts on crop productivity. Long-term fertilizer experiments were initiated in the early 1970s, and they have been periodically evaluated since then. The continuous, long-term use of chemical fertilizers and manures is highly influencing the chemical nature and fertility status of the soil.

Understanding the suitable alloying conditions for highly efficient Cu- and Mn-doped Zn1-xCdxS/ZnS core-shell quantum dots

Doping of alloyed colloidal quantum dots (QDs) has garnered significant attention for providing tunable and Stokes-shifted emission. By alloying the host semiconductor nanocrystals (NCs), their band gap can be tuned. With the specific addition of dopant ions, these NCs can emit tunable emissions within the visible spectrum. However, while doped and alloyed quantum dots (QDs) have shown promise for tunable emissions, their emission qualities have not been consistent across the spectrum. Here, we report the synthesis of high-quality Cu- and Mn-doped ZnxCd1-xS (x = 0–1) alloyed QDs by a colloidal non-injection method. In this study, we examined the effect of different dopant ions on the optical properties of similar alloyed nanocrystals. The deposition of a ZnS shell on these doped QDs significantly improves their quantum yield (QY), increasing it from 7.0 % to 50.0 % for Cu-doped QDs and from 30.0 % to 80.0 % for Mn-doped QDs. The Cu-doped QDs exhibit tunable emission from green to red across the visible spectrum by varying the Zn/Cd ratio, whereas the Mn-doped QDs show a fixed orange emission. Interestingly, the Cu-doped alloyed QDs show a contrasting trend in quantum yield (QY) compared to those of Mn-doped QDs when the amount of Cd in ZnCdS alloyed QDs is systematically changed. As the amount of Cd increases in the ZnCdS alloyed QDs, the Cu-doped QDs show both an increase in average lifetime and an increase in QY. In contrast, for the Mn-doped QDs, the decay lifetime values remain fairly constant for different amounts of Cd in the ZnCdS alloyed QDs, but the QY decreases as the amount of Cd increases. The results of this study may facilitate the design of optimal alloying combinations for Cu/Mn-doped QDs in optoelectronic applications.

Liquefaction of Hongliulin coal asphaltenes and conversion of oxygenic structure

Based on the multistage liquefaction process, asphaltene was prepared from Hongliulin coal and deeply liquefied with Ni-Co-Mo, Co-Mo, and Fe2O3 catalysts. The yields of each component were obtained. Ultimate analysis, FT-IR and XPS was used to investigate the conversion of oxygenic structure. The results showed that the ability to catalyze asphaltene conversion was ranked as Ni-Co-Mo > Co-Mo > Fe2O3 in the range of 440–480 °C. The highest oil yield was 44.31% at 470 °C with Ni-Co-Mo catalyst. Ni-Co-Mo catalyst could effectively increase the H/C ratio and reduce the oxygen content of asphaltene, which had a strong correlation with the trend of oil yield. The main forms organic oxygen in remaining asphaltene after liquefaction were C = O, C-O and COO and the contents decreased sequentially. Ni-Co-Mo could catalyze the cleavage of C = O significantly to form a large amount of C-O and improve the oil yield after 460 °C. The C-O content was about 34% in remaining asphaltene, which was mainly a relatively stable aromatic ether structure and was not susceptible to hydrocracking reactions.

Geospatial modeling of hydrological ecosystem services in an ungauged upper Yamuna catchment using SWAT

Hydrological ecosystem services (HES) are vital for resource allocation, conservation prioritization, and climate change adaptation. However, research gaps persist due to limited understanding of complex hydrological systems and inadequate ground-station data, especially in ungauged watersheds with complex terrains. The present study addresses these gaps by estimating and mapping HES descriptors using regionalization techniques in an ungauged Aglar watershed. Additionally, it conducts a temporal analysis of hydrological fluxes using Soil and Water Assessment Tool (SWAT). Aglar is a constituent sub-watershed of a gauged watershed named Bausan having two discharge observation stations located at Naugaon and Bausan sites in Uttarakhand, India. A nested parameterization approach was adopted to represent the hydrological variabilities over the Bausan watershed with one outlet at Aglar. The stream flow derived from the calibrated Bausan watershed was used as synthetic observations in SWAT model setup for Aglar and found to yield very good statistical results. Calibration yielded coefficient of determination (R2) = 0.91, Nash Sutcliff Efficiency (NSE) = 0.91, and standardized root mean square error (RSR) = 0.29, while the validation yielded R2 = 0.68, NSE = 0.50, and RSR = 0.37. Parameters related to base flow or stream flow were the most sensitive in the model's output. Water balance analysis reveals 36% of precipitation transformed into stream flow, with direct runoff accounting for 72% and base flow for 28%. Forest cover contributed approximately 50.68% of precipitation through evapotranspiration. The study identifies a maximum sediment load of approximately 26 t/ha, indicating fragility of the landscape. Non-parametric tests such as Mann Kendall and Sen's slope indicated increasing trends in surface runoff, lateral flow, water yield, and groundwater recharge. The analysis of the empirical cumulative distribution function demonstrated that all hydrological components exhibit trends similar to precipitation. Spatially and temporally, variations in HES provisioning were observed, with forests surpassing non-forest areas. These findings emphasize the value of HES descriptors in analyzing spatiotemporal changes in HES provisioning and offer valuable insights for policymakers for future policy dialogues. This study lays the groundwork for further investigations into the hydrological processes of ungauged watersheds.

Analyzing Spatial Dependence of Rice Production in Northeast Thailand for Sustainable Agriculture: An Optimal Copula Function Approach

Rice is not only central to Thailand’s economy and dietary consumption but also plays a significant role in global food security. Northeast Thailand, in particular, is a principal region for rice cultivation. However, with the mounting concerns of climate change, it becomes paramount to understand the interplay between regional weather patterns and rice yields, aiming to develop effective adaptive agricultural strategies. The current study aimed to fill the research gap by investigating an optimal copula for the spatial dependence of rice production and related meteorological variables in this area. The objective of this study is to understand how rice production in different areas relates to each other in order to improve farming practices and address challenges such as suitable weather. To achieve this goal, we apply three families of copulas—elliptical, Archimedean, and extreme—to analyze crop and meteorological variables across the watershed in the northeastern region of Thailand. With a data foundation extending from 1981 to 2021 from the Regional Office of Agricultural Economics Sector 4, Thailand, this study offers a comprehensive analysis of the spatial dynamics driving rice production across twenty provinces in Northeast Thailand. Using a piecewise linear model, we dissected rice yield trends, revealing distinct slopes in production and yield across various periods. The analysis leaned on elliptical, Archimedean, and extreme copula families, using the maximum likelihood estimation to discern marginal distribution residuals. Through rigorous bootstrap goodness-of-fit tests and cross-validation, the most appropriate copula for each province was identified. Key findings demonstrate pronounced spatial interdependencies in rice yields, with the Frank copula prominently capturing the product relationship between provinces such as Maha Sarakham and Roi-Et. Conversely, the Clayton copula better characterized regions such as Srisaket and Ubon Ratchathani. Moreover, the results underscore the considerable influence of meteorological factors, notably rainfall and temperature, on rice production, especially in regions like Ubon Ratchathani. In distilling these multifaceted relationships, the study charts a pathway for crafting sustainable, localized agricultural strategies. As the world grapples with climate change’s ramifications, the insights from this research stand crucial, offering direction for fostering resilience, adaptation, and optimizing rice productivity across Thailand’s diverse agrarian landscapes.

Theoretical Study on the Formation Mechanism of NOx Precursors During the Pyrolysis of 2,4-Imidazolinediketone

ABSTRACT The incineration technology of food waste is one of the effective technologies for achieving waste resource utilization and harmless utilization. As the most fundamental and important stage in the incineration process, it is necessary to conduct detailed research on the pyrolysis mechanism. Therefore, this article selects 2,4-imidazolidinedione as the representative chemical compound for food waste. Study the pyrolysis mechanism of 2,4-imidazolidinedione and the reaction pathway of NOx precursor generation by using density functional theory (DFT) calculation.The possible paths of NH3, HCN and HNCO formation from C-N/C-C bond fracture and C-N bond direct fracture caused by H-transfer are proposed. The formation path of NH3 has low energy barrier and high reaction rate constant at low temperature. At the same time, it explained that the release of HCN increases with the increase of temperature. In addition, this study has also theoretically demonstrated that the reaction path of NH3 is more prone to occur compared to HCN, which finely interprets the yield trend of NH3 and HCN in the experiment.

Effect of straw return on soil respiration in dryland agroecosystem of China: A meta-analysis

In agroecosystems, straw return is considered an important agricultural management practice influencing soil respiration. However, the impact of straw return on soil respiration is equivocal and the underlying mechanisms underpinning any responses remain unclear. To address this, we synthesized 86 recently published papers on dryland agroecosystems in China, which comprise approximately 74% of the agricultural land area, using a meta-analysis approach and explored the influence of straw return on soil respiration and the underlying mechanisms involved. Straw return resulted in a 13.5% increase in soil respiration, a 9.4% increase in soil moisture, a 10.4% increase in crop yield, a 18.9% increase in SOC (soil organic carbon), and a 2.6% decrease in soil temperature. Compared to conventional tillage without straw return treatment, the increase in soil respiration in response to straw return was closely related to modifications in soil temperature, soil moisture, SOC, and crop yield (P < 0.05). The contribution of the different drivers to increases in soil respiration in response to straw return showed a trend of crop yield > soil temperature > SOC > soil moisture. In addition, straw mulching, crop planting system, tillage method, and fertilization modified the effect of straw return on soil respiration. The increase in soil respiration was less when straw was used as a mulch (12.9%), compared to when it was incorporated into the soil (18.8%). The cropping system also influenced the effect of straw return on soil respiration and was increased by 21.1% in inter-crops, and by 17.3% and 11.0% in monocultures and rotations treatments, respectively. Our results also showed that the effect of straw return on soil respiration was larger in deep tillage treatments than in shallow tillage treatments (26.9% vs.18.7%). In addition, the effect of straw return on soil respiration was also larger in fertilized treatment compared to unfertilized one (12.7% vs. 0.5%). However, this depended on the amount of nitrogen fertilizer applied rates and was higher (14.8%) with low (<300 kg ha−1) application rates compared to high (≧300 kg ha−1) ones (2.5%). Therefore, in dryland agroecosystems in China, biological factors, especially those related to crop yield, may be a more important factor regulating the effect of straw return on soil respiration. However, straw mulching, crop planting system, tillage method, and fertilization can modify the effect of straw return on soil respiration.

Condensation of residue during direct liquefaction of a high-vitrinite coal

To understand the formation of residues and its relation with the vitrinite structure and the products yield in direct coal liquefaction (DCL), a high vitrinite low-rank coal, Naomaohu (NMH), is studied at 380–460 °C in the presence of tetrahydronaphthalene and absence of a catalyst. The structures of coal and residues are characterized by the proximate and ultimate analyses, 13C nuclear magnetic resonance, and the electron spin resonance. Beside the frequently seen coal conversion and products yield at various temperatures and time, the products yield and the residues structure parameters are correlated with the residues yield (YRes) and the quantity of hydrogen donated from tetrahydronaphthalene (QH). It is found that the DCL can be roughly defined into two regions demarcated by 420 °C, with limited condensation at 380–400 °C and intensive condensation at 440–460 °C, for example. These two-region behaviors can be clearly evidenced by the different trends in products yield and the residues parameters, such as those relevant to the cleavage of bridge bonds (fal, Cn, δ-C and δ-O) and those relevant to the residues’ condensation (far, Xb, RD and H/C), against YRes and QH. The two-region behavior is important for optimization of DCL conditions and its processing scheme.

Modification of extended chemical percolation devolatilization model for application to low-rank coals

For understanding coal gasification reactions, primary pyrolysis reaction is important as the first stage of the gasification reactions. Recently, the authors developed a primary pyrolysis model to predict tar formation behavior by extending the chemical percolation devolatilization (CPD) model which is often used to predict pyrolysis behavior. The extended CPD (Ex-CPD) model predicts gas and tar components as respective chemical species. Consequently, the secondary tar decomposition behavior can be calculated by elementary step-like reaction models. However, some of the coal structure parameters used in the model contradict the 13C NMR data. That is, the molecular weight of the aromatic ring clusters calculated by the original Ex-CPD decreases with the aromatic index obtained by 13C NMR. In this study, this problem was resolved by modifying the calculation procedure to determine the coal structure parameters. The recalculated aromaticity index agreed with that calculated from the 13C NMR data. The modified Ex-CPD model was validated through comparison with coal gasification experiments using a pressurized drop tube furnace. The calculation results successfully described the trend of light gases, soot, and char yield in the experiments.

A weakly supervised framework for high-resolution crop yield forecasts

Predictor inputs and labels (e.g. yield data) for crop yield forecasting are not always available at the same spatial resolution. Common statistical and machine learning methods require inputs and labels at the same resolution. Therefore, they cannot produce high resolution (HR) yield forecasts in the absence of HR yield data. We propose a weakly supervised (WS) deep learning framework that uses HR inputs and low resolution (LR) labels (crop areas and yields) to produce HR forecasts. The forecasting model was calibrated by aggregating HR forecasts and comparing with LR crop area and yield statistics. The framework was evaluated by disaggregating yields from parent statistical regions to sub-regions for five countries and two crops in Europe. Similarly, corn yields were disaggregated from counties to 10 km grids in the US. The performance of WS models was compared with naive disaggregation (ND) models, which assigned LR forecasts for a region or county to all HR sub-units, and strongly supervised models trained with HR yield labels. In Europe, all models (ND, WS and strongly supervised) were statistically similar, mainly due to the effect of yield trend. In the US, the WS models performed even better than the strongly supervised models. Based on Kendall’s rank correlation coefficient, the WS model forecasts captured significant amounts of HR yield variability. Combining information from WS with Trend model (using LR yield trend) and WS No Trend model (not using yield trend) provided good estimates of yields as well as spatial variability among sub-regions or grids. High resolution crop yield forecasts are useful to policymakers and other stakeholders for local analysis and monitoring. Our weakly supervised framework produces such forecasts even in the absence of high resolution yield data.

Harvest index has increased over the last 50 years of maize breeding

Quantifying historical changes in maize harvest index (HI), the fraction of above-ground biomass allocated to grain yield, can enhance our ability to explain grain yield trends and estimate stover carbon inputs for sustainability assessments. However, the HI genetic gain has not been the primary focus of previous era studies. The aim of this study is to enhance our knowledge of maize HI genetic gain. Our first objective is to quantify HI genetic gain in Bayer Crop Science Legacy hybrids and investigate the contribution of breeding and agronomic management. Our second objective is to develop a general-use model to describe the temporal evolution of maize HI. We studied 54 commercial hybrids (103-day and 111-day relative maturities) released from 1983 to 2020 across 13 environments, including plant density (current and historical increasing rate) and N-fertilizer (low and sufficient N rates) treatments. The HI was estimated at physiological maturity by destructively sampling plants. Then we synthesize new experimental data with literature findings (n = 16) to provide a robust HI genetic gain estimate. Results showed that HI has increased over the years from 0.516 to 0.571 in 103-day hybrids and from 0.537 to 0.584 in 111-day hybrids. The genetic gains were similar across environments and management treatments within the studied range, indicating that this increase is attributed to maize breeding. The N-fertilizer treatments affected the magnitude of the HI, but plant density did not. Our results, combined with 16 literature datasets, revealed a 0.26% year−1 relative increase in HI since 1964. We estimated that the increase in HI accounts for ca. 15% of the historical maize yield increase in the US Corn Belt over the past 50 years. The maize HI has increased over the last 50 years, and this increase was attributed to breeding, not to management. Our findings enhance our knowledge of maize HI, will support robust estimations of carbon inputs in sustainability studies, and inform crop models to better capture historical yield increases.

Study on Predicting the Price of Coconut in Tamil Nadu Market

Aims: The study aims to comprehensively analyze coconut cultivation trends in Tamil Nadu, covering area, production, yield, and market prices. Through data analysis, it seeks to reveal historical shifts in cultivation practices. The study also strives to develop precise price forecasting models for Coimbatore and Pollachi markets, enhancing insights into agricultural dynamics and enabling accurate price predictions.&#x0D; Study Design: Exploratory research design.&#x0D; Place and Duration of Study: The present study was conducted in the year 2023 during July and August&#x0D; Methodology: Secondary data was used for the present study. Secondary Data on area , production and productivity of coconut in Tamil Nadu was collected from 2000 to 2022 and price of coconut from 2012 to 2023 in selected market was collected. The research utilizes Compound Annual Growth Rate (CAGR) Analysis to identify the past trends in the area, production, and productivity of coconut in Tamil Nadu. Additionally, the Autoregressive Integrated Moving Average (ARIMA) models are employed to forecast the future trens in coconut price in Coimbatore and Pollachi market.&#x0D; Results: Area, Production and Productivity of coconut in Tamilnadu has shown a positive trend. Coconut cultivation is growing steadily with a 1.56% increase in cultivation area and a 2.089% rise in production, while productivity is also improving by 1.19% annually, indicating positive growth. ARIMA (1, 1, 1) for Coconut in the Coimbatore market and ARIMA (1, 1, 1) for coconut in the Pollachi market was found to provide the best fit for predicting the price of coconut in both markets.&#x0D; Conclusion: Increase in trend in area, production and productivity was observed in Tamil Nadu. ARIMA (1, 1, 1) was found to provide the best fit for predicting the price of coconut in both markets. The model predicted declining price in the forthcoming months (August – September, 2023). Increased area under cultivation and increased production of coconut in all the major growing States are the major reasons for declining prices. Efforts have to be made to increase the consumption of coconut value added products to sustain the price of coconut in the coming years.

Simulated impact of fill tube geometry on recent high-yield implosions at the National Ignition Facility

Inertial confinement fusion capsules fielded at the National Ignition Facility are filled with deuterium and tritium fuel by means of a fill tube. The fill tube introduces a low-density pathway into the fuel region of the capsule that allows high Z contaminant to invade the hot spot during the course of the implosion. A recent series of nominally identical high-yield implosions on the NIF has exhibited significant variability in performance. We evaluate the impact of the fill tube in these implosions computationally to determine whether variations in fill tube geometry could have contributed to this variability. The main contrast between the fill tube geometry in the six shots was the outer diameter of the capsule bore hole, a conical hole into which the fill tube is inserted. In our simulations, the geometry of the bore hole can play a significant role in the development of nonlinear flows seeded by the fill tube. We find that the amount of space between the bore hole and the fill tube is the primary factor that determines the amount of contaminant jetted into the hot spot by the fill tube and, in turn, the level of yield reduction due to the fill tube in our simulations. As a consequence, some capsules with 5 μm fill tubes are predicted to outperform capsules with 2 μm fill tubes. We also find that micrometer-scale changes to bore hole size can impact fusion yields by up to four times near the ignition threshold. Nevertheless, simulation trends do not reproduce experimental yield trends, suggesting that the fill tube geometry was not the primary factor contributing to the observed variability in performance and that the fill tube could be masking sensitivity to other asymmetries such as other micrometer-scale capsule defects like voids that were not included in our simulations.

A semiparametric spatio‐temporal model of crop yield trend and its implication to insurance rating

AbstractWe demonstrate the benefit of spatial smoothing for crop trend estimation with a deterministic spatio‐temporal trend model. The proposed model is semiparametric, where the parametric temporal trend is modeled with a two‐knot spline function for forecasting robustness, and the nonparametric spatially‐varying coefficients are modeled by the radial basis function method for flexibility. To select the smoothing parameter of our trend model, we propose a forward validation criterion tailored to meet the forecasting nature of rating crop insurance. This criterion is based on a rolling regression approach that adds one year of data at a time for validation. We also propose a new criterion for model comparison using relative mean squared error in forecasting insurance payouts. Our empirical results show that the proposed trend model is more efficient and capable of identifying profitable insurance policies than two competing models in most state‐crop combinations.

Effect of Slow-Release Urea Partial Replacement of Soybean Meal on Lactation Performance, Heat Shock Signal Molecules, and Rumen Fermentation in Heat-Stressed Mid-Lactation Dairy Cows.

This study aimed to assess the effects of partially substituting soybean meal in the diet with slow-release urea (SRU) on the lactation performance, heat shock signal molecules, and environmental sustainability of heat-stressed lactating cows in the middle stage of lactation. In this study, 30 healthy Holstein lactating dairy cattle with a similar milk yield of 22.8 ± 3.3 kg, days in milk of 191.14 ± 27.24 days, and 2.2 ± 1.5 parity were selected and randomly allocated into two groups. The constituents of the two treatments were (1) basic diet plus 500 g soybean meal (SM) for the SM group and (2) basic diet plus 100 g slow-release urea and 400 g corn silage for the SRU group. The average temperature humidity index (THI) during the experiment was 84.47, with an average THI of >78 from day 1 to day 28, indicating the cow experienced moderate heat stress conditions. Compared with the SM group, the SRU group showed decreasing body temperature and respiratory rate trends at 20:00 (p < 0.1). The substitution of SM with SRU resulted in an increasing trend in milk yield, with a significant increase of 7.36% compared to the SM group (p < 0.1). Compared to the SM group, AST, ALT, and γ-GT content levels were significantly increased (p < 0.05). Notably, the levels of HSP-70 and HSP-90α were significantly reduced (p < 0.05). The SRU group showed significantly increased acetate and isovalerate concentrations compared with the SM group (p < 0.05). The prediction results indicate that the SRU group exhibits a significant decrease in methane (CH4) emissions when producing 1 L of milk compared to the SM group (p < 0.05). In summary, dietary supplementation with SRU tended to increase the milk yield and rumen fermentation and reduce plasma heat shock molecules in mid-lactation, heat-stressed dairy cows. In the hot summer, using SRU instead of some soybean meal in the diet alleviates the heat stress of dairy cows and reduces the production of CH4.

Genetic progress in seed and fiber quality traits of cotton in Argentina

Context:The Argentinian national cotton (Gossypium hirsutum L.) breeding program has demonstrated a 3.24 kg ha−1 y−1 genetic progress in lint yield from 1965 to date. Although previous studies have examined different trends in cotton lint yield, lint quality parameters, and other agronomic traits of cultivars over time, there have been no investigations in Argentina analyzing the genetic progress in the different components of technological fiber quality, cottonseed oil and protein composition. Objectives:(i) to estimate the genetic progress of the lint and seed quality parameters among cultivars released in Argentina since 1965, (ii) to evaluate the variation of genetic progress for the lint and seed quality parameters under different environmental conditions, and (iii) to determine the correlations between lint quality and lint yield traits. Method:This study was conducted over a two-year period, involving 20 cotton cultivars grown in four different environments (achieved through varying sowing dates) in Reconquista, Santa Fe, Argentina. Several fiber variables were evaluated, including upper half mean length (UHML), strength (Str), uniformity index (UI), micronaire (Mic), short fiber index (SFI), and spinning consistency index (SCI), as well as seed measurements, such as cottonseed oil content (Oil), protein content (Prot), and cottonseed oil fatty acid composition. Results:Significant differences were observed in all the quality parameters evaluated according to the environment and genotype, highlighting the importance of agronomic management in improving the different quality parameters. Oil content in the genotypes and years studied showed significant increases over time. Likewise, the maintenance of quality parameters despite the significant increases in lint yield and some of its components is considered a positive result for the Argentinian breeding program. Pearson correlation analysis showed that LY was positively correlated with UHML, UI, SCI, oil, and linoleic acid, whereas LY was negatively correlated with SFI, and both stearic and oleic acids. Conclusions:The data obtained from this study would serve as a basis for future research to explore the mechanisms responsible for altering or increasing both the fiber quality parameters and the concentrations of these by-products to improve cotton quality in Argentina.

Corn and soybean planting order decisions impact farm gross revenue

AbstractThe inter‐annual corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation field is a well‐known management practice that increases the yield of both crops across the midwestern United States. Each spring, farmers must decide which crop will be planted first. Prioritizing the planting of one crop can delay planting of the other, which can result in substantial yield loss and reduced associated revenue. The objective of this work was to assess how gross farm revenue (corn + soybean acres) can be affected by crop planting order (corn first, soybean second, and vice versa). The impact of variable planting dates on the yield of each crop was simulated for 310 fields across the United States. Gross farm revenue was estimated as a function of crop planting date, order, input costs and crop prices. In a randomly chosen field in south central Wisconsin, 1 out of the 310, delaying planting after May 1 reduced yield of each crop and subsequently suppressed gross farm revenue. Crop planting order determined farm revenue due to a variable loss in per day yield rate within the nominal planting timeframe associated with the two crops. In addition, the degree to which management intensified for each crop relative to crop yield potential accruing with earlier planting varied by state and further impacted farm revenue. Overall results suggest that to determine planting order, US farmers need to be aware of the comparative yield trends associated with delayed planting of corn vs. soybean for their specific farms and cropping systems and should also account for projected crop selling prices.

The effect of agricultural credit on cassava output and yield in nigeria.

The broad objective of this study is the analysis of the long and short run effects of agricultural credit on cassava yield in Nigeria. The study applied descriptive statistics and autoregressive distributed lag (ARDL) econometric) approach to the secondary data collected from Central Bank of Nigeria Statistical Bulletin and FAOSTAT. The descriptive analysis shows that cassava output was increasing in Nigeria with decreasing trend in yield. It was revealed in the study that Roots and Tubers Expansion Programme (RTEP) has no significant effect on cassava production and yield. The result of the econometric analysis indicates that agricultural credit do not have significant long and short-run effects on cassava production and yield. This is because the real per capita agricultural credit is small. However, study indicates that fertilizer has long and short-run effects on cassava output but does not have a significant effect on cassava yield. This study also documented evidence of declined labour and land productivity on the long and short-run. The study reveals that ecessive rainfall can be detrimental to cassava production in the long and short-run. The study concludes that although, agricultural credit do not have significant long and short-run effects on cassava production and yield, it is positively correlated with agricultural inputs used in cassava production. The study recommended the need to address the decline in the real value of per capita credit by increasing the amount of credit given to the farmers. There is also the need to design more credit facilities suitable to the farmers. The current Anchor Borrowers programme should be designed to accommodate more resource-poor cassava farmers.

NITROGEN, POTASSIUM, AND SILICON FERTILIZATION TO ACHIEVE LOWER PANICLE BLAST SEVERITY AND IMPROVE YIELD COMPONENTS OF RICE USING RESPONSE SURFACE METHODOLOGY

Rice blast is one of the most critical limiting factors for rice plant growth performance, and it occurs in 85 countries, causing 10-35% grain yield losses. Several findings have indicated the positive benefits of nitrogen (N), potassium (K), and Silicon (Si) fertilization on plant development, yield, and biotic stress relief. However, due to rice blast attacks, its growth, development, and yield may be restricted or limited by insufficient or unbalanced N, K, and Si fertilizers. This study was conducted to optimize the fertilization strategies for rice panicle blast control and improve rice grain yield. The methods used were Central Composite Design and Response Surface Methodology. The application of N, K and Si did not influence the number of spikelets per meter square, filled grain (%) and 1000- grain weight (g). An increase in K and Si significantly reduced the rice blast severity in the off-season 2021 and the main-season 2021/2022. On the other hand, only Si had influenced rice grain yield production. An increase in Si showed a positive linear trend in rice grain yield. Based on these results, panicle blast disease is expected to be controlled with the recommended rate of 104 N kg/ha, 42 P2O5, 80 kg K2O, and an additional 200 Si kg/ha, which minimizes the rice blast severity (%) but at the same time maximizes the rice grain yield. The findings of this study provide a scientific base and technical advice for high-yield rice grain-growing under panicle blast disease hot spot areas.