Yield Variability Research Articles

Nitrogen application increased yield sensitivity of indica hybrid rice to climate resource

Rice yield variability can be attributed to the range of sowing dates across extensive cropping areas and varying nitrogen application rates. Nonetheless, the precise quantitative relationship between sowing dates, nitrogen application rates, and rice yield development remains elusive. In this study, we evaluated differences in dry matter and yield components of three indica hybrid varieties for five sowing dates at four ecological sites in the middle and lower reaches of the Yangtze River, China in 2019 and 2020, with three nitrogen application rates. Results indicated that yield initially increased and then decreased with delayed sowing dates. Photosynthetically active radiation accumulation after anthesis (PARAA) was identified as the key positive climate resource affecting rice yield, decreasing by an average of 19.7 MJ m−2 for every 15-day delay in sowing. Nitrogen agronomic efficiency decreased linearly with the delay in sowing dates. Early sowing of rice with high PARAA and effective accumulated temperature after anthesis (EATAA) showed yield increases due to nitrogen application, whereas late-sown rice with insufficient photothermal resources did not exhibit marked yield improvement and may experience yield reduction. Ensuring adequate dry matter accumulation after anthesis and high seed setting rate was the main way to increase yield, influenced by sowing dates and nitrogen application rates. Intriguingly, our study revealed that nitrogen application markedly enhanced yield sensitivity to PARAA and EATAA. This finding underscores the pivotal role of nitrogen in modulating crop responsiveness to climate resources, and contributes to a deeper understanding of agronomic practices for optimizing yield under varying climatic conditions.

Non-enzymatic methods for isolation of stromal vascular fraction and adipose-derived stem cells: A systematic review.

Adipose-derived stem cells (ADSCs) and the stromal vascular fraction (SVF) have garnered substantial interest in regenerative medicine due to their potential to treat a wide range of conditions. Traditional enzymatic methods for isolating these cells face challenges such as high costs, lengthy processing time, and regu-latory complexities. This systematic review aimed to assess the efficacy and practicality of non-enzymatic, mechanical methods for isolating SVF and ADSCs, comparing these to conventional enzymatic approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive literature search was conducted across multiple databases. Studies were selected based on inclusion criteria focused on non-enzymatic isolation methods for SVF and ADSCs from adipose tissue. The risk of bias was assessed, and a qualitative synthesis of findings was performed due to the methodological heterogeneity of the included studies. Nineteen studies met the inclusion criteria, highlighting various mechanical techniques such as centrifugation, vortexing, and ultrasonic cavitation. The review identified significant variability in cell yield and viability, and the integrity of isolated cells across different non-enzymatic methods compared to enzymatic procedures. Despite some advantages of mechanical methods, including reduced processing time and avoidance of enzymatic reagents, the evidence suggests a need for optimization to match the cell quality and therapeutic efficacy achievable with enzymatic isolation. Non-enzymatic, mechanical methods offer a promising alternative to enzymatic isolation of SVF and ADSCs, potentially simplifying the isolation process and reducing regulatory hurdles. However, further research is necessary to standardize these techniques and ensure consistent, high-quality cell yields for clinical applications. The development of efficient, safe, and reproducible non-enzymatic isolation methods could significantly advance the field of regenerative medicine.

Production performance of major vegetables in Maharashtra

The present study was restricted to only six crops namely brinjal, tomato, potato, onion, cabbage, and cauliflower which had the highest production in the year 2020-2021 amongst the vegetable crops in Maharashtra. The study was based on secondary data pertaining to the period 2001-2002 to 2020-21.In onion, there was a significant increasing trend of growth in area and production, while in potato significant increasing trend of growth was observed in production and yield, likewise tomato showed a significant increasing trend of growth in area and brinjal showed a significant increasing trend of growth in yield. The area, production, and yield of cabbage and cauliflower were not significant at all.Highest variability in area and production was observed in onion and cabbage, respectively while highest inconsistency of yield was seen in potato. Lest variability in area, production and yield was observed in brinjal. The instability in area was high in cabbage and cauliflower, medium in tomato, potato and onion, and low in brinjal. While in production, it was high in cabbage and cauliflower, medium in potato, tomato, onion and brinjal. Also, in yield, it was medium in potato and onion and low in brinjal, tomato, cabbage and cauliflower.

Plant genotype and inoculation with indigenous arbuscular mycorrhizal (AM) fungi modulate wheat productivity and quality of processed products through changes in the frequency of root AM fungal taxa

ContextArbuscular mycorrhizal fungi (AMF) have been extensively applied as biofertilizers in wheat to promote crop productivity. However, variability in AM root colonization, grain yield, and nutrients was observed among wheat genotypes and according to AM genotype and environment. ObjectivesWe hypothesized that wheat response to AM inoculation is more affected by genotype than environment; the response is driven by increases in AM abundance and community structure changes, and not by modification of composition. MethodsWe inoculated an indigenous AM consortium on four old genotypes (Bianco Nostrale, Andriolo, Abbondanza, Sieve) and one modern variety (Bologna) of bread wheat for two years. The effect was evaluated by assessing grain yield, nutrients, and quality of processed products (flour and breadsticks), while the AM abundance and the community composition and structure in roots were characterized, at two plant growth stages, using morphological and molecular tools. ResultsThe functional traits of AMF and plant were better explained by inoculation than by genotype or environment (33 %, 17 %, 4 % of total explained variance), although significant interactions environment x genotype and genotype x inoculation were highlighted. Consistent increases in AM abundance in Sieve and Bologna were associated with positive changes in grain yield and nutrients, supporting the good responsiveness of these genotypes with inoculated AMF, while the plant response of other genotypes was shaped by air temperature and rainfall. However, we did not find significant correlations between changes in AM colonization and mycorrhizal response ratio, with the exception of P and K. After inoculation, AM community composition was similar in all wheat genotypes, but the structure greatly differed among genotypes in interaction with inoculation and plant growth stage. These changes were significantly related to wheat productivity. A Septoglomus taxon, present in the inoculum, was the best predictor of wheat performance. The characterization of the community structure at early crop development and maturity allowed the identification of fast and latest active AM colonizers. Our results showed for the first time that AM inoculation affect the rheological parameters and nutraceuticals of processed products, although the response was modulated by genotype. ConclusionsThe selection of responsive wheat genotypes is fundamental for the positive outcome of inoculation. The positive effects on wheat productivity and field persistence of the inoculated AMF support the use of indigenous consortia that have low impacts on resident AMF. SignificanceOur findings advance the understanding of the facilitative mechanisms that underlie compatibility between AMF and wheat genotypes.

Unraveling Genetic Variability, Correlation and Path Analysis for Yield and Its Components in Barley (Hordeum vulgare L.)

The current study aimed to assess the genetic variability of yield and yield-related traits while examining the direct and indirect effects of trait interactions. Conducted at a research farm in Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India, the experiment involved fourteen parents and their BC1F1 crosses, analyzed using a randomized block design (RBD). Significant differences were observed among the varieties for most traits, indicating a broad range of mean values and diversity. Both phenotypic and genotypic coefficients of variance (PCV and GCV) were generally low, with biological yield showing the highest values (26.57 for GCV and 40.48 for PCV). PCV values were slightly higher than GCV values. Traits such as seed per spike, biological yield, flag leaf breadth, days of heading, and days of maturity exhibited relatively high heritability. Notably, grain yield showed high heritability and genetic progress, making it a favourable selection indicator. Several traits, including days of anthesis, days of maturity, and spike length, demonstrated significant positive correlations with yield while also showing significant negative correlations with grain filling period and biological yield. Conversely, these traits showed highly non-significant positive correlations with harvest index, plant height, thousand seeds weight, and chlorophyll content and highly non-significant negative correlations with tiller number and seeds per spike.

Identification and Validation of New Reference Genes for Normalization of Gene Expression in Flower and Berry Developmental Stages of Interspecific Grape Hybrid V. vinifera (L.) × V. labrusca (L.)

Abstract In quantitative reverse transcription polymerase chain reaction (qRT-PCR), normalizing target gene expression using a reference gene is an indispensable step to control the variability of RNA extraction yield, RNA integrity, reverse transcription efficiency, and PCR amplification. In the present study, we identified candidate reference genes with stable expression during grapes’ flowering and berry development stages. Ten genes, including ACT, CYP5, RLI, TUB, UBC, UBC17, UBC60, UFD1, VAG, and ZNF with relatively stable expression, were selected based on RNAseq data generated earlier in grape hybrid ‘ARI 516’. The expression of these candidate genes was tested at different stages of flowering and grape berry development. Five different algorithms such as RefFinder, geNorm, NormFinder, BestKeeper, and the comparative ΔCq method were used to test the expression stability of candidate genes. A comprehensive ranking obtained by RefFinder showed that UBC17, RLI, and ZNF were the most stable reference genes during flower and berry development stages. UBC17, RLI, and ZNF were calibrators to normalize the expression of VvAGL11 as a target gene to validate the worthiness of identified reference genes. The result demonstrated that newly identified reference genes could be successfully used to normalize the expression of the target gene accurately. These reference genes will provide more choices for selecting appropriate reference genes to normalize gene expression in grapes.

STUDY OF GENETIC VARIABILITY OF LENTIL (<i>Lens culinaris</i> Medik) GERMPLASM FOR MORPHOLOGICAL, PHYSIOLOGICAL AND YIELD TRAITS

Lentil (Lens culinaris Medik) is one of the oldest crops in the world, an autogamous, diploid species and one of the most common grain legume crops. Lentil is a significant member of the Fabaceae Family. The majority of its production occurs in marginal lands with rain-fed conditions. Lentils have a strong impact on genetics and alter yield percentages in loamy soil. Lentil production and acreage have declined badly during the last ten years. During 2022–2023, a field experiment was conducted at the Pulses Research Area of the Ayub Agricultural Research Institute in Faisalabad. Two hundred genotypes with four slandered checks were tested in this research i.e Masoor - 93, Pb M-2009, Markaz-2009, and Pb M-2019. Markaz-2009 was approved variety of NARC, and the other three checks were commercial varieties developed by PRI Faisalabad. Two hundred genotypes used in the experiment, were carried out in rows of four meters each, with a row-to-row spacing of 30 cm and a plant-to-plant 10cm spacing. The variability in yield, heritability, genetic development, and relationships among the 200 accession of lentil germplasm were determined. Principal component analysis (PCA) was performed to analyses the data. The significant outcomes and variations for all attributes among genotypes expressed by the principal component analysis were observed. Estimated heritability requirements ranged from 30 to 50% for plant height, pods per plant, plant biomass, days to 90% maturity, days to 50% flowering, and days to seed production. Plant height, number of branches per plant, and number of pods per plant all were significantly correlated positively with yield, whereas plant stand, days to 90% maturity and plant biomass significantly correlated negatively with yield per hectare. Days to 50% flowering and seed production have a strong positive relationship with days to 90% maturity. The results revealed significant yield variance and traits that affect yield for the genotypes studied, which will aid in the improvement of lentil genotypes in the area. From 200 accessions, the PLL-15501, ILL-1697-1939, PLL-15508, PLL-15504, and PLL-13515 lines displayed the highest plant height, whereas the AARIL-08515, AARIL-04533, PLL-10511, PLL-14518, and PLL-15501 lines displayed the highest number of branches per plant. Maximum pods per plant shown in entries like AARIL-98502, PLL-10507, PLL-13515, PLL-14522, PLL-15502, and PLL-19502. The data statistically analyzed to identify the best genotypes for the characters previously described.

Irrigation of young grapefruits with desalinated seawater: Agronomic and economic outcomes

Given the current scarcity of freshwater resources, it is imperative to explore new agricultural management options to sustainably enhance food production. Desalinated seawater (DSW) presents a promising solution for irrigation in water-stressed regions. However, its application in perennial crops has been poorly assessed, potentially posing challenges to existing cultivation practices due to higher associated costs, salinity, and the presence of potentially harmful elements, notably boron (B). To address these uncertainties, a three-year experiment was conducted to evaluate the short-term effects of irrigation with DSW on a ‘Rio Red’ grapefruit orchard. Four irrigation treatments were assessed: DSW, freshwater (FW), a 1:1 mixture of DSW and FW (MW), and DSW with reduced B concentration (DSW–B). At present, the young age of the trees (3.5 years) and their grafting onto a five-year-old rootstock at the beginning of the experiment likely facilitated rapid foliar mass development and prevented the accumulation of phytotoxic elements up to critical levels. However, local DSW consistently exceeded recommended citrus thresholds for B (0.5 mg L–1), sodium (Na+; 115 mg L–1), and chloride (Cl–; 250 mg L–1) in irrigation water, resulting in significant concentrations of B (2.1 mg kg–1), Na+ (504 mg L–1) and Cl– (476 mg L–1) in soil. Moreover, these levels led to concentrations in leaves close to defined thresholds in the case of Na+ (0.25 g 100 g–1), and exceeded them in the case of B (>250 mg kg–1). Although fruit quality remained unaffected, variability in yield among trees and the cost disparity between water resources, resulted in slight fluctuations in the income-outcome balance during initial cultivation years. Our findings offer insights into the irrigation of sensitive crops with DSW, aimed at mitigating potential soil and plant harm from early accumulation of phytotoxic elements. Further research is warranted to explore the impact of both single and sustained DSW usage for irrigation purposes.

Genetic variability and path coefficient analysis among bread wheat (Triticum aestivum L.) genotypes for yield and yield‐related traits in bale highlands, southeastern Ethiopia

AbstractWheat (Triticum aestivum L.) is one of the most important strategic crops to overcome food security problems in the world including Ethiopia. This can be achieved through genetic improvement of the crop and its production practices. In this connection, information on the mutual association of traits is important for effective selection in wheat‐breeding programs. In 2021/2022 production year, a field experiment was undertaken to estimate the extent of genetic variability, path analysis, and association of yield and yield‐related characteristics for the selection of desirable wheat genotypes. A total of 64 bread wheat genotypes, including yellow rust‐resistant Boru (resistant), Hachalu (moderately resistant), and Madda‐Walabu (susceptible) as control, were used for the field experiment at Sinana and Agarfa Agricultural Research Centers. The experiment was laid out in a simple alpha lattice design. The mean analysis of variances showed highly significant differences (p < 0.001) for all traits among the tested genotypes. The value of the phenotypic coefficient of variance was higher than the genotypic coefficient of variance for all studied traits, indicating the presences of higher genetic variability among the tested genotypes. Grain yield had a significant positive correlation with days to maturity, grain‐filling period, plant height, kernels spike−1, thousand seed weight, and biological yield at both genotypic and phenotypic levels including spike length at phenotypic levels at both locations and with plant height, thousand seed weight, and biological yield at both locations at genotypic and phenotypic levels. Similarly, days to maturity and grain‐filling period also showed a significant positive phenotypic correlation with grain yield. The analyzed path coefficients at both genotypic and phenotypic levels also indicated how grain yield is influenced directly and indirectly by other yield and yield‐related traits. The result also showed the existence of good heritability values along with high genetic advance, higher phenotypic coefficient of variation and genotypic coefficient of variation for grain yield, biological yield, and grain‐filling period. This implies the true relationship among these traits and grain yield; therefore, due attention should be paid to such traits during selection for further improvement. Hence, the present study indicated the importance of the direct and indirect effects of the studied traits on yield and yield components which makes selection desirable for improving the grain yield of wheat and its genetic resistance against yellow rust in the study area.

ОЦЕНКА СТАБИЛЬНОСТИ УРОЖАЙНОСТИ ЯРОВОГО ЯЧМЕНЯ

The purpose of research is to determine the share of influence of factors in the overall variability of barley yield and assess stability using parametric and non-parametric statistical methods in the conditions of the south of the Moscow Region. According to the results of the study (2020–2022), significant contributions to yield variability were identified: environmental conditions – 81.9 %; genotypic characteristics – 7.4 and genotype × environment interaction – 8.2 %. 7 samples (280.7–345.2 g/m2) were classified as excee¬ding the standard. Evaluation by parametric (bi – regression coefficient; S2dᵢ – deviation from regression; θi – average variance component; θ(i) – GE variance component; Wi2 – environmental valence; σi2 – stability variance; CV – coefficient of variation) and nonparametric (S(1,2,3,6) – rank statistics; NP(i) – non-parametric stability statistics; KR – rank sum) stability indicators allowed us to identify differences in the response of the sample under study to environmental conditions. A high correlation was noted between S(3) and NP(4) (r = 0,90), S(1) and S(2) (r = 0,96), S(6) and NP(4) (r = 0,98), Wi2 and σi2, (r = 1,00), Wᵢ2 and θᵢ (r = 1,00), σ2ᵢ and θᵢ (r = 1,00), Wᵢ2 and θ(i) (r = –1,00), σ2ᵢ, and θ(i) (r = –1,00), θᵢ and θ(i) (r = –1,00). Relationship with productivity in such indicators as S(1), S(2), S(3), NP(1), Wᵢ2, σ2ᵢ, S2dᵢ, CV, θᵢ, θ(ᵢ) was weak, the greatest association was noted with bᵢ (r = 0,60), NP(3) (r = –0,67), NP(4) (r = –0,67), S(6) (r = –0,69), KR (r = –0,71), NP(2) (r = –0,78). Samples with a combination of stability and high yield were identified: Alei (k-31363, Russia), Maximus (k-31366, Russia), Austris (k-31368, Latvia), Povolzhsky Luch (k-31392, Russia).

Exploring the interaction of weed control management and crop structure on maize yield in the wide range of Mexican cropping systems.

Abstract Herbicide use as the main driver to control weeds is increasingly being questioned. Several countries have adopted policies to control the use of herbicides, challenging multiple actors to restore, expand, or develop alternative solutions for weed management that consider the specific needs of each agroecosystem. Testing alternative weed management (agroecological) in a wide range of cropping systems can include comparing them with herbicide use and exploring their potential interaction with different cropping systems and cropping structures. The objectives of our study were: i) to assess weed control treatments in different cropping systems (CSYS), ii) to explore interactions between weed treatment and cropping structure (CSTR) and investigate how these interactions affect weed and maize covers evolution, and iii) to assess weed control treatments and CSTR effects on final grain yield. Weed management treatments did not differ in grain yield in a wide range of CSYS. Agroecological management required a greater number of practices. The timing of weed control practices (Ve and V6) was an important factor for both agroecological and herbicides treatments and for different genotypes. Weed management treatments did not interact with CSTR to define grain yield. However, it explained between 28 to 60% of grain yield variability. We highlight the importance of genotype and plant density elections for grain yield determination. We also highlight that the apparent response of genotypes and density on weeds in experimental conditions is not evident in field conditions. It is still necessary to develop more complex models that can include factors such as the decision-making of farmers and technicians.

Towards food security: Exploring the spatio-temporal dynamics of soybean in India

India is the world’s largest edible oil importer, and soybean oil accounts for a major portion of those imports, with implications for the Indian economy. Despite being the 4th largest globally in terms of harvested soybean area and 5th largest in terms of production, India is still heavily dependent on imports to meet the vegetable oil requirement for its population. It is therefore imperative to understand the dynamics and trends in India’s soybean production to help the country achieve self-sufficiency in edible oils. This study provides the first spatially explicit analysis of soybean in India, using long-term spatial and temporal statistics at national and subnational levels, using spatial and temporal statistical analysis models to examine the historical trends and its future prospects. Our analysis details the overall soybean expansion across the country and the increase in production but we also note that the annual growth rate has declined in each consecutive decade even though the area continues to expand. The average national yield has been stagnant at around 1 T/Ha but for some of the low-producing districts, a higher yield of more than 3 T/ha is reported. For most major producing districts, soybean yields are below 1.5 T/Ha. The state of Madhya Pradesh which was the major soybean producer is now matched by the state of Maharashtra in terms of production, however, Madhya Pradesh still has the largest area under soybean. We analyzed soybean hotspot expansion in India and found that the mean center of the soybean area and production has shifted approximately 93 km towards the south and 24 km to the west as the crop is rapidly being adopted in the southern and western parts of India expanding the hotspot in these parts. District-level analysis showed that the total number of districts constituting hotspots of soybean cultivation in India has increased from 29 to 42 in three decades. Furthermore, analysis of soybean oil and meal consumption with respect to the national population, import, export, domestic production, GDP per capita, and price of soybean oil and meal suggests that soybean oil and meal are highly correlated with GDP per capita and population, indicating that consumption of soybean oil and meal is likely to increase as GDP per capita increases, and future demand is expected to rise with the anticipated growth in the Indian population. Increased soybean production can play a significant role in increasing national food security for India and reducing dependence on foreign oil imports and also help the economy with soy meal exports. Understanding the spatiotemporal variability in area and yield will help target interventions to increase production. Given the overall low yields with high variability in production, particularly in recent years primarily due to successive extreme rains and droughts in major producing districts and the overall need to increase production to meet the country’s demand, there is a pressing need for government policies and research aimed at narrowing the yield gap and developing soybean varieties that are more productive and resilient to climate change.

Global de-trending significantly improves the accuracy of XGBoost-based county-level maize and soybean yield prediction in the Midwestern United States

ABSTRACT The application of machine learning in crop yield prediction has gained considerable traction, yet uncertainties persist regarding the impact of the yield trends on these predictions and the differences between the detrending methods. In our study, we utilized extreme gradient boosting (XGBoost) to scrutinize the effects of no trend processing (NTP), input year as a feature (IYF), input average yield as a feature (IAYF), input linear yield as a feature (ILYF), and the global detrending method (GDT) on the yield prediction of maize and soybean in the Midwestern United States. Based on our findings, compared with that of NTP, the incorporation of the yield trend as a predictor in XGBoost significantly improved the accuracy and reduced the uncertainty of the yield prediction. Notably, GDT emerged as a standout performer, significantly reducing the average yield prediction error by 0.091 t/ha for soybean and 0.158 t/ha for maize with respect to NTP, and concurrently improving the determination coefficient (R2) by 20.6% and 19.6% for soybean and maize, respectively. Compared with IYF, IAYF, and ILYF, GDT showed substantial improvements ranging from 3.8% to 12.7% in R2 for soybean and 3.6% to 12.7% for maize. The SHapley Additive ExPlanations (SHAP) framework showed that the enhanced vegetation index (EVI), particularly during the soybean podding and maize dough formation stages, played a crucial role in understanding the variations in interannual yield variability. These findings confirmed the importance of GDT in crop yield prediction via machine learning and could be used to facilitate future advancements in machine learning applications for yield forecasting.

Utilizing Machine Learning Framework to Evaluate the Effect of Climate Change on Maize and Soybean Yield

In recent years, climate change patterns and extreme weather events have adversely affected agricultural production and have raised concerns about its effect on crop yields. These changes can affect the crop yield in many ways including the changes in the length of the growing season, planting and harvest time windows, precipitation amount and frequency, and the growing degree days, etc. So, it is important to analyze the effect of climate change on yield variability for a better understanding of its effect on the crops. This study aims to analyze the historical monthly county-level data for the state of Ohio to quantify maize (Zea mays) and soybean (Glycine max) yield temporal variability and to study the impact of proposed climate change scenarios on the yield. Machine learning algorithms were used to model the effect of monthly temperature and precipitation levels on yield variability, and to study the effect of climate change scenarios on yield. In addition, yield prediction models were integrated with proposed climate change scenarios for higher and lower emissions scenarios to predict the maize and soybean yield for the year 2100. To model the effect of weather parameters on temporal yield variability, the Random Forest model outperformed with RMSE of 0.61 Mt/ha and R2 of 0.73 for maize and with RMSE of 0.21 Mt/ha, R2 of 0.64 for soybean crop. Maximum temperature for July which has a negative correlation with yield was found to be the most dominant parameter for maize yield followed by the precipitation in August, July, and June which have a positive correlation with maize yield. Precipitation in August was found to be the most dominant weather parameter for soybean yield followed by maximum temperature in July. Based on the projected average increase in Ohio’s temperature and precipitation levels alone, maize yield was predicted to drop by 13.2% or 18.5% respectively due to lower emissions and higher emissions climate change scenario of Ohio. Similarly, a 6.64% and a 9.63% drop is expected in the soybean yield by the year 2100 for both lower emissions and higher emissions climate change scenarios. In economic terms based on current commodity values, this is an astounding loss of 254.7 to 369.7 million USD for maize and 535.9 – 751.1 million USD for soybean crop to the state of Ohio.

The Clinical Translation of α-humulene - A Scoping Review.

α-humulene, a sesquiterpene found in essential oils of various plant species, has garnered interest due to its potential therapeutic applications. This scoping review aims to consolidate α-humulene's evidence base, informing clinical translation, and guiding future research directions. A scoping review was conducted of EMBASE, MEDLINE, and PubMed databases up to 14th July 2023. All studies describing original research on α-humulene extraction, as well as pre-clinical and clinical research, were included for review. Three hundred and forty articles were analysed. α-humulene yields ranged from negligible to 60.90% across plant species. In vitro experiments demonstrated cytotoxicity against adenocarcinomas (such as colorectal, pulmonary, breast, prostatic, lung, and ovarian), with varying responses in other cell models. Mechanistic insights revealed its involvement in mitochondrial dysfunction, diminished intracellular glutathione levels, and the induction of oxidative stress. In rodent studies, oral administration of α-humulene at 50 mg/kg reduced inflammation markers in paw oedema and ovalbumin-induced airway inflammation. Intraperitoneal administration of α-humulene (50 - 200 mg/kg) exhibited cannabimimetic properties through cannabinoid 1 and adenosine A2a receptors. α-humulene also exhibited a multitude of properties with potential scope for therapeutic utilisation. However, there is a paucity of studies that have successfully translated this research into clinical populations with the associated disease. Potential barriers to clinical translation were identified, including yield variability, limited isolation studies, and challenges associated with terpene bioavailability. Consequently, rigorous pharmacokinetic studies and further mechanistic investigations are warranted to effectively uncover the potential of α-humulene.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Winegrape Yield and Revenue Variability in Australia

While winegrowers usually want to achieve consistent yield targets, there is a high degree of yield and price (and hence gross revenue) variability in winegrape production. The aim of this study was to determine whether there are differences in yield and revenue variability across climates, varieties, and regions in Australia. This was performed by estimating statistical models of the impact of these three variables on the coefficient of variation of yield and gross revenue per hectare. The results suggest that hotter and drier regions exhibit lower interannual yield variability, something that in the past may have been largely explained by the use of irrigation, but which may change in the future with climate change and higher water prices. The results also showed that there are sometimes differences in yield and revenue variability, not only across regions, but also between varieties.

Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones

Abstract. Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM. However, the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral-associated organic matter (MAOM) at the subfield scale within nine farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. In contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas and that there was no significant difference in the relative contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks.

Exploring the genetic variability in yield, nutritional and digestibility traits in oat grains through ruminant nutrition

Oat is a dual-purpose crop used for both food and feed for animals. The objective of this work is to characterize oat varieties for their genetic diversity in yield, physical traits, and nutritional composition, aiming to identify potential parent varieties for breeding programs to develop new oat varieties for improved livestock feed and diverse industrial applications. To conduct, chemical analysis for protein and carbohydare fractions, energy and digestible nutrient estimated, stastical analyses performed to assess genetic variations for traits among vaieties. Significant genetic variation (p < 0.05) for grain yield, grain density, sieving percentage, crude protein, ether extract, neutral and acid detergent fiber, cellulose, lignin, neutral and acid detergent insoluble nitrogen were observed in grains of eight oat varieties. All protein fractions exhibited significant differences (p < 0.05). Total carbohydrate content ranged significantly (p < 0.05) from 73 % to 79 %. The grains contained higher levels of intermediately degradable starch and pectin (54.12–60.16 %) compared to the slowly degradable cell wall (26–33 %), lignin bounded cell wall (6–10 %), and rapidly degradable sugars (2–8%). Significant variation (p < 0.05) was observed in terms of gross energy, digestible energy, metabolizable energy, net energy for maintenance and lactation about (2 Mcal/kg dry matter), gain (1.6–1.8 Mcal/kg dry matter), total digestible nutrients, digestible dry matter, rumen degradable protein, and total digestible nutrients related to crude protein, fatty acid, neutral detergent fiber, and non-fiber carbohydrate. Organic matter and ether extract were positively associated (p < 0.01) with total digestible nutrients, digestible and metabolizable energy, dry matter digestible and truly digestible non fibrous cabohydrates, while neutral and acid detergent fiber and cellulose showed negative correlation. The research shows that oat varieties vary widely in their yield, physical features, and nutritional content, offering potential for breeding better varieties for both animal feed and industrial uses.

Análise espacial da produtividade da cenoura irrigada em função das propriedades físicas do solo

ABSTRACT Understanding soil, plant attributes, and carrot crop dynamics is vital for optimizing site-specific management practices and enhancing crop yield and soil quality. This study aimed to ascertain the dependence and spatial variability between irrigated carrot yield and soil physical attributes in a clayey Oxisol. The research took place during the 2021 growing season in a commercial carrot production area in Campos Altos, MG, Brazil. One hundred sampling points were established within the irrigated area, distributed across a square expanse of 40,000 m², positioned at the centers of 20 × 20 m grids. The study revealed high yield in the irrigated carrot crop (99.74 t ha-1) with moderate variability. Soil attributes displayed a range of heterogeneity, ranging from low to extremely high, contingent on the variable assessed, as determined by the coefficient of variation. Among the variables exhibiting spatial dependence, particle density exhibited a minimum range of 40 m, which is recommended for further studies on irrigated carrot cultivation in a clayey Oxisol. Total soil water availability demonstrated a positive spatial correlation and proved to be the recommended variable for estimating the spatial variability of carrot crop yield.