Biological Constraint Research Articles

Disentangling the Factors Selecting for Unicellular Programmed Cell Death.

AbstractThe widespread occurrence of genetically programmed cell death (PCD) in unicellular species poses an evolutionary puzzle. While kin selection theory predicts that the fitness benefits of cell suicide must be preferentially directed toward genetic relatives, it does not predict the nature of these benefits. Furthermore, cell suicide must be conditionally expressed, leaving open the question of what conditions optimally regulate expression. Here we formalize several verbal hypotheses for the ecological function of unicellular PCD. We show that self-sacrifice by healthy cells cannot evolve. Instead, PCD evolution requires that damaged cells sense impending death and then (1) expedite this death to spare resources for groupmates, (2) prepare cellular contents so that necrotic toxins are not released upon death, or initiate autolysis in order to (3) release beneficial compounds or (4) release anticompetitior toxins. The prerequisite ability to predict death is a severe cell biological constraint as well as an ecological constraint that restricts PCD evolution to species with specific sources of mortality. We show that the specific type of PCD that will evolve, though, differs on the basis of a species' ecology, life history, and genetic structure.

Temporal heterogeneity in cognitive architectures

In 2020, Mc Fadden published an article in which he discusses how algorithms can be encoded in time and space. By analyzing the topology of the cytoarchitecture of the brain, cognitive architectures can understand the underlying mechanisms that have led to the development of human intelligence in space. In this study, our focus lies in investigating temporal heterogeneity as a mechanism that the brain could have developed not solely as a biological constraint, but also as an evolutionary advantage. To accomplish this, we employed virtual agents within a virtual environment and constructed a prototype cognitive architecture. Subsequently, we compared the benefits and drawbacks of having this cognitive architecture operate under a model of temporal heterogeneity versus one characterized by temporal homogeneity. At the conclusion of the article, we present the results obtained from two perspectives. From a quantitative standpoint, we contrast the agents’ adaptation to the environment based on the cognitive architecture model employed by each agent. On this front, we found evidence that temporal heterogeneity might be useful in finding parameter optimizations faster, amongst other benefits. From a qualitative perspective, we examine the potential of this model to explore the cognitive processes of the virtual agents, concluding that a different representation of percepts is needed, which we further discuss.

Post-Emergence Chemical Weed Control in Direct Seeded Rice (Oryza sativa L.) in New Alluvial Zone of West Bengal

Direct seeded rice (DSR) seems to be a viable option under erratic rainfall and water scarcity. Despite several advantages of DSR, weeds are the major biological constraint. Afield experiment was conducted at Central Research Farm, Gayeshpur of Bidhan Chandra Krishi Vishwavidyalaya Mohanpur, Nadia, West Bengal during the kharif season ((July- November) of 2020 and 2021, comprisingnine weed control methods viz. T1- Imazethapyr 10% SL @ 1000 ml ha-1at 15 DAS, T2 - Imazethapyr 10% SL @ 1250 ml ha-1at 15 DAS ,T3- Imazethapyr 10% SL @ 2000 ml ha-1at 15 DAS, T4- Imazethapyr 10% SL @ 750 fb 750 ml ha-1at 15 DAS and 30 DAS, T5- Imazethapyr 10% SL @ 1000 fb 1000 ml ha-1at 15 DAS and 30 DAS, T6- Imazethapyr 10% SL @ 1250 fb 1250 ml ha-1at 15 DAS and 30 DAS, and T7- Imazethapyr 10% SL @ 2000 fb 2000 ml ha-1at 15 DAS and 30 DAS, T8- weed free check (2 times hand weeding at 20 DAS and 40 DAS) and T9- un-treated control (weedy check) along with 3 replicationslaid out in RBD design in herbicide tolerant rice variety Sava 134 was conducted during two consecutive kharif season of 2020 and 2021 at Central Research Farm, Gayeshpur, B.C.K.V., Nadia under new alluvial zone of West Bengal. The experimental results revealed that maximum grain and straw (4.20 and 4.36 t per ha), as well as net return (Rs. 44546per ha) were recorded in double hand weeding (T8), which was at par with Imazethapyr treated plots like T5, T6 and T7. Thus the tedious, time-consuming, and costly hand-weeding can be profitably replaced by the application of Imazethapyr 10% SL @ 1000 fb 1000 ml per ha at 15 and 30 DAS. This weed control methodhas satisfactory performance in DSR exhibiting higher weed control efficiency and no phytotoxicity symptoms to the herbicide tolerant rice crop and increased both grain and straw yields vis-à-vis benefit : cost ratio.

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.

Predicting airborne ascospores of Sclerotinia sclerotiorum through machine learning and statistical methods

AbstractA main biological constraint of dry bean (Phaseolus vulgaris) production in Canada is white mould, caused by the fungal pathogen Sclerotinia sclerotiorum. The primary infectious propagules of S. sclerotiorum are airborne ascospores and monitoring the air for inoculum levels could help predict the severity of white mould in bean fields. Daily air samples were collected in commercial dry bean fields in Alberta, Manitoba and Ontario and ascospores were quantified using quantitative PCR. Daily weather data was obtained from in‐field weather stations. The number of ascospores on a given day was modelled using 63 different environmental variables and several modelling methods, both regression and classification approaches, were implemented with machine learning (ML) (random forests, logistic regression and support vector machines) and statistical (generalized linear models) approaches. Across all years and provinces, ascospores were most highly correlated with ascospore release from the previous day (r ranged from 0.15 to 0.6). This variable was also the only variable included in all models and had the greatest weight in all models. Models without this variable had much poorer performance than those with it. Correlations of ascospores with other environmental variables varied by province and sometimes by year. A comparison of ML and statistical models revealed that they both performed similarly, but that the statistical models were easier to interpret. However, the precise relationship between airborne ascospore levels and in‐field disease severity remains unclear, and spore sampling methods will require further development before they can be deployed as a disease management tool.

Challenges and Alternatives of Herbicide-Based Weed Management

Weeds are the most severe and widespread biological constraint on agricultural production systems and cause damage to cropped and non-cropped lands. They reduce crop yield and degrade the quality of the produce, besides raising the cost of production. The intensification of agriculture in the Green Revolution era attracted chemical fertilizers and dwarf varieties coupled with mono-cropping and irrigation practices, which enhanced crop-associated weeds and the widespread use of herbicides for easy control. Pesticides may kill many organisms, both target and non-target species, in the environment, causing an imbalance in the ecosystem. Despite the significant increase in productivity, the environmental repercussions of industrial agriculture, characterized by the use of high-yielding crop varieties and the extensive application of chemical fertilizers and pesticides, have prompted a quest for more sustainable agricultural practices worldwide. One potential alternative lies in innovative approaches that draw upon ecological insights gleaned from studying natural ecosystems. These approaches aim to create “ecologically intensive” agro-ecosystems. Developing ecologically intensive agro-ecosystems necessitates a deep understanding of the biological dynamics within ecosystems and the integration of traditional agricultural knowledge held by local farmers. Considering the potentiality of appropriate weed management technologies to substantially improve crop productivity, there is an opportunity for the development, popularization, and adoption of effective, economical, and eco-friendly weed management technologies.

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation.

Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.

Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization

Deep Joint Source-Channel Coding for DNA Image Storage: A Novel Approach With Enhanced Error Resilience and Biological Constraint Optimization

Differentiable optimization layers enhance GNN-based mitosis detection

Automatic mitosis detection from video is an essential step in analyzing proliferative behaviour of cells. In existing studies, a conventional object detector such as Unet is combined with a link prediction algorithm to find correspondences between parent and daughter cells. However, they do not take into account the biological constraint that a cell in a frame can correspond to up to two cells in the next frame. Our model called GNN-DOL enables mitosis detection by complementing a graph neural network (GNN) with a differentiable optimization layer (DOL) that implements the constraint. In time-lapse microscopy sequences cultured under four different conditions, we observed that the layer substantially improved detection performance in comparison with GNN-based link prediction. Our results illustrate the importance of incorporating biological knowledge explicitly into deep learning models.

Targeted and selective knockout of the TLQP-21 neuropeptide unmasks its unique role in energy homeostasis

ObjectivePro-peptide precursors are processed into biologically active peptide hormones or neurotransmitters, each playing an essential role in physiology and disease. Genetic loss of function of a pro-peptide precursor results in the simultaneous ablation of all biologically-active peptides within that precursor, often leading to a composite phenotype that can be difficult to align with the loss of specific peptide components. Due to this biological constraint and technical limitations, mice carrying the selective ablation of individual peptides encoded by pro-peptide precursor genes, while leaving the other peptides unaffected, have remained largely unaddressed. MethodsWe developed and characterized a mouse model carrying the selective knockout of the TLQP-21 neuropeptide (ΔTLQP-21) encoded by the Vgf gene. To achieve this goal, we used a knowledge-based approach by mutating a codon in the Vgf sequence leading to the substitution of the C-terminal Arginine of TLQP-21, which is the pharmacophore as well as an essential cleavage site from its precursor, into Alanine (R21→A). ResultsWe provide several independent validations of this mouse, including a novel in-gel digestion targeted mass spectrometry identification of the unnatural mutant sequence, exclusive to the mutant mouse. ΔTLQP-21 mice do not manifest gross behavioral and metabolic abnormalities and reproduce well, yet they have a unique metabolic phenotype characterized by an environmental temperature-dependent resistance to diet-induced obesity and activation of the brown adipose tissue. ConclusionsThe ΔTLQP-21 mouse line can be a valuable resource to conduct mechanistic studies on the necessary role of TLQP-21 in physiology and disease, while also serving as a platform to test the specificity of novel antibodies or immunoassays directed at TLQP-21. Our approach also has far-reaching implications by informing the development of knowledge-based genetic engineering approaches to generate selective loss of function of other peptides encoded by pro-hormones genes, leaving all other peptides within the pro-protein precursor intact and unmodified.

Phylogeny and evolution of erythrocytes in mammals.

In the past century, several authors have investigated the allometry of haematological parameters in mammals. As haematocrit and haemoglobin (Hb) concentration are almost constant within the Mammalia (although with notable exceptions), differences in other haematological parameters are mainly reducible to red blood cell size (mean corpuscular volume, MCV). Past studies testing for correlation between MCV and body mass have given contradictory results. Using phylogenetically informed regressions, here I demonstrate that the correlation between MCV and body mass is indirect, and is in reality due to the correlation between MCV and basal metabolic rate. This could be explained by the fact that small erythrocytes allow a fast release of oxygen in tissues with high metabolic demand. Nonetheless, hypoxia-adapted species show MCV greater than that predicted by their metabolic rate, while Ruminantia show the inverse. Interestingly, these species show the highest and lowest, respectively, Hb affinity for oxygen. In the present paper, I suggest that Hb-oxygen affinity, acting as a biological constraint for oxygen exchange, determines the size of red blood cells. Hb intrinsic affinity for oxygen shows little variation during evolution and modifying the levels of allosteric factors can be viewed as an adaption to adjust Hb-oxygen affinity to metabolic demands (the same also happens during ontogeny). Nonetheless, in some lineages, mutations raising Hb-oxygen affinity allowed some species to colonize hypoxic environments; in Ruminantia, instead, there was a drastic decrease, which cannot be adaptively explained.

Variable Region Sequences Influence 16S rRNA Performance.

16S rRNA gene sequences are commonly analyzed for taxonomic and phylogenetic studies because they contain variable regions that can help distinguish different genera. However, intra-genus distinction using variable region homology is often impossible due to the high overall sequence identities among closely related species, even though some residues may be conserved within respective species. Using a computational method that included the allelic diversity within individual genomes, we discovered that certain Escherichia and Shigella species can be distinguished by a multi-allelic 16S rRNA variable region single nucleotide polymorphism (SNP). To evaluate the performance of 16S rRNAs with altered variable regions, we developed an in vivo system that measures the acceptance and distribution of variant 16S rRNAs into a large pool of natural versions supporting normal translation and growth. We found that 16S rRNAs containing evolutionarily disparate variable regions were underpopulated both in ribosomes and in active translation pools, even for an SNP. Overall, this study revealed that variable region sequences can substantially influence the performance of 16S rRNAs and that this biological constraint can be leveraged to justify refining taxonomic assignments of variable region sequence data. IMPORTANCE This study reevaluates the notion that 16S rRNA gene variable region sequences are uninformative for intra-genus classification and that single nucleotide variations within them have no consequence to strains that bear them. We demonstrated that the performance of 16S rRNAs in Escherichia coli can be negatively impacted by sequence changes in variable regions, even for single nucleotide changes that are native to closely related Escherichia and Shigella species; thus, biological performance is likely constraining the evolution of variable regions in bacteria. Further, the native nucleotide variations we tested occur in all strains of their respective species and across their multiple 16S rRNA gene copies, suggesting that these species evolved beyond what would be discerned from a consensus sequence comparison. Therefore, this work also reveals that the multiple 16S rRNA gene alleles found in most bacteria can provide more informative phylogenetic and taxonomic detail than a single reference allele.

Effect of combined application of carbonate salts and hot water treatment for the management of postharvest anthracnose (Colletotrichum gloeosporioides) of papaya.

Postharvest anthracnose (Colletotrichum gloeosporioides Penz.Sacc) is the most economically important biological constraint to papaya production and consumption, which causes substantial yield loss worldwide. The effect of combined application of carbonate salts and hot water treatments on the development of postharvest anthracnose and maintenances of postharvest quality of papaya fruit was studied in completely randomized design (CRD) under laboratory condition. The results revealed that combined application of hot water treatment and carbonate salts significantly (p < .05) reduced the incidence and severity of postharvest papaya anthracnose disease. The disease incidence reached 100% 21 days after inoculation in the control treatment; this level was significantly reduced to 26.70% by dipping the fruits in NH4CO3 at 50°C and NH4CO3 at 54°C. Similarly, treatments significantly (p < .05) reduced the disease severity in different degrees from the first day of disease appearance to the date of 100% unmarketability of control fruits. Furthermore, the combined application of carbonate salt and hot water treatments significantly improves fruit marketability by 93.33%. Moreover, the treatments showed significant (p < .05) effect on maintaining pH, TSS, TA, and reducing postharvest weight loss of papaya fruit. In conclusion, postharvest treatment of papaya fruit with NH4CO3 at 54°C, NH4CO3 at 52°C, and NaCO3 at 54°C can significantly reduce anthracnose development and improve marketability of the fruits without pronounced effect on their edible qualities.

Headstarting turtles to larger body sizes for multiple years increases survivorship but with diminishing returns

AbstractHeadstarting is a conservation tool that assumes raising turtles in protected ex situ environments to larger body sizes, then releasing them back into the wild, increases their survivorship compared to wild, non‐headstarted turtles. Our goal was to quantitatively test this fundamental assumption by comparing somatic growth and survivorship among 3 age classes of headstarted juvenile wood turtles (Glyptemys insculpta) monitored for 3 years (2016–2018) in Ontario, Canada. Our age classes were turtles headstarted for 2 years (n = 15), turtles headstarted for 1 year (n = 30), and turtles incubated and hatched ex situ, then released (i.e., no headstarting; hatchlings, n = 30). Both age classes of headstarted turtles were radio‐tracked for 1 year after release. We released hatchlings in August and radio‐tracked them for 1 month. All cohorts exhibited positive somatic growth after release. One‐month post‐release survival of hatchlings was 70%. Turtles headstarted for 2 years had slightly higher 1‐year post‐release survivorship (67%) than turtles headstarted for 1 year (47%), but there was little to no evidence for a difference (P = 0.17). Modeling these survivorship results with real‐life constraints of only 100 hatchlings available for headstarting each year (biological constraint from source population), and a 100‐turtle capacity in our headstarting facility (economic constraint), projected similar population growth in 2‐year and 1‐year headstarting programs. When removing the 100‐turtle facility limit, the 2‐year program projected higher population growth but required space for 200 turtles in the headstarting facility, which may not be feasible for many programs. Our results support the fundamental assumption that headstarting increases survivorship, but we observed diminishing returns if headstarting was increased for longer than 1 year. Given the growing number of turtle headstarting projects globally, our study provides data that can aid in establishing cost‐ and resource‐effective best practices.

Mapping quantitative trait loci and predicting candidate genes for Striga resistance in maize using resistance donor line derived from Zea diploperennis.

The parasitic weed, Striga is a major biological constraint to cereal production in sub-Saharan Africa (SSA) and threatens food and nutrition security. Two hundred and twenty-three (223) F2:3 mapping population involving individuals derived from TZdEI 352 x TZEI 916 were phenotyped for four Striga-adaptive traits and genotyped using the Diversity Arrays Technology (DArT) to determine the genomic regions responsible for Striga resistance in maize. After removing distorted SNP markers, a genetic linkage map was constructed using 1,918 DArTseq markers which covered 2092.1cM. Using the inclusive composite interval mapping method in IciMapping, twenty-three QTLs influencing Striga resistance traits were identified across four Striga-infested environments with five stable QTLs (qGY4, qSC2.1, qSC2.2, qSC5, and qSC6) detected in more than one environment. The variations explained by the QTLs ranged from 4.1% (qSD2.3) to 14.4% (qSC7.1). Six QTLs each with significant additive × environment interactions were also identified for grain yield and Striga damage. Gene annotation revealed candidate genes underlying the QTLs, including the gene models GRMZM2G077002 and GRMZM2G404973 which encode the GATA transcription factors, GRMZM2G178998 and GRMZM2G134073 encoding the NAC transcription factors, GRMZM2G053868 and GRMZM2G157068 which encode the nitrate transporter protein and GRMZM2G371033 encoding the SBP-transcription factor. These candidate genes play crucial roles in plant growth and developmental processes and defense functions. This study provides further insights into the genetic mechanisms of resistance to Striga parasitism in maize. The QTL detected in more than one environment would be useful for further fine-mapping and marker-assisted selection for the development of Striga resistant and high-yielding maize cultivars.

Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines

Cellular utilization of available energy flows to drive a multitude of forms of cellular "work" is a major biological constraint. Cells steer metabolism to address changing phenotypic states but little is known as to how bioenergetics couples to the richness of processes in a cell as a whole. Here, we outline a whole-cell energy framework that is informed by proteomic analysis and an energetics-based gene ontology. We separate analysis of metabolic supply and the capacity to generate high-energy phosphates from a representation of demand that is built on the relative abundance of ATPases and GTPases that deliver cellular work. We employed mouse embryonic fibroblast cell lines that express wild-type KRAS or oncogenic mutations and with distinct phenotypes. We observe shifts between energy-requiring processes. Calibrating against Seahorse analysis, we have created a whole-cell energy budget with apparent predictive power, for instance in relation to protein synthesis.

Management of Foliar and Soil Borne Diseases in Long Pepper using Bioagents and Organics

Long pepper (Piper longum L.) is an important medicinal plant used in Ayurvedic system of medicine since ancient time and diseases are an important biological constraint in limiting the production and productivity of it hence a field experiment was conducted to evaluate the foliar and soil borne diseases of Long pepper (cv. Vishwam) using several combinations of bioagents and organics. The result revealed that soil treatment with T. viride @3 g/m2 with 20t FYM/ha + Neem Cake @ 2t/ha was found superior in managing the leaf blight/rot disease where as spraying of Tebuconazole + Trifloxystrobin @ 0.1% was appeared to be the best in case of leaf spot and stem rot diseases. As far as dry catkin yield is concerned, soil treatment with T. viride @3 g/m2 with 20t FYM/ha + Neem Cake @ 2t/ha recorded highest yield of 445.4 Kg/ha.

Searching of Novel Herbicides for Paddy Field Weed Management—A Case Study with Acetyl-CoA Carboxylase

Weed management is the major biological constraint in paddy (Oryza sativa L.) producing areas. Predominantly, barnyard grass (Echinochloa crus-galli) is a rice-mimicking weed that causes 57% of yield loss in rice production. Conventionally, herbicides are the site-specific weed inhibitors often used to suppress E. crus-galli growth. Acetyl-CoA carboxylase (ACCase) is an important target for developing novel herbicides with remarkable selectivity against gramineous weeds. Notably, fenoxaprop-P-ethyl (FPPE) is a selective ACCase herbicide extensively used in paddy fields to inhibit barnyard grass. However, prolonged use of FPPE herbicide elicits phytotoxicity in cultivated rice and herbicide resistance in weeds. Recently, phytotoxins are emerging as an alternative to commercial herbicides with safer environmental profiles. Nevertheless, discovering natural herbicides through in vivo and in vitro techniques is time-consuming and expensive. Therefore, high-end computational screening strategies including Tanimoto similarity, docking, binding free energy, and herbicide-likeness were used to pinpoint the lead molecule. Finally, molecular dynamics and MM/PBSA calculations were employed to validate the binding kinetics of the hit compound. Indeed, sinigrin was identified as a promising phytotoxic inhibitor against the ACCase enzyme. The findings of our study were well correlated with the existing experimental results. Overall, the current work will aid in the development of commercializing phytotoxin herbicides in foreseeable future.

Understanding Strategic Weed Management in Direct Seeded Rice - A Review

Transplanting in puddled soils (intensive tillage in ponded conditions) with continuous flooding is the most common method of rice crop establishment in Asia. However, the most important problem associated with transplanted rice is that change in soil aggregates and development of hardpan below the soil surface caused due to flooding and puddling, which is not desirable for following wheat crop. Although, this is advantageous for effective weed control in the transplanted rice field. The technique of flooding the field with water is not only very laborious, tidy, and cumbersome but also very expensive, and time-consuming. The direct seeding of rice seems to be the only viable alternative to liberate farmers. However, weeds are the main biological constraint in DSR. Weed problems associated with DSR (direct seeded rice) have been explained in this review paper and also the strategies to develop for weed management in DSR. In this effort the chapter has been covered by elaborating the techniques of prevention, land preparation, effect of sowing time on weed dynamics, impact of planting density on weed dynamics, impact of row spacing, mechanical weed control, nutrient management, water management, chemical weed control and integrated weed management in context to DSR with a view to achieve enhanced rice yield similar to that under transplant condition.

Weed Management in Oilseeds-A Holistic Perspective: A Review

Weeds are the major biological constraint in oilseed crops. Yield loss in oilseeds vary from 20 to 77 per cent, depending on the crop, intensity of weed infestation, duration of crop competition, soil and climatic factors. Weed interference, not only reduces the seed yield, but also reduces the oil yield and quality. Critical period of weed control in oilseeds are generally from 15 to 45 DAS. Weed removal during the critical period is essential to reduce the yield loss. Hike in wage rate and phytotoxicity of herbicides calls for an integrated approach for weed management in oilseeds. Instead of relying on a single method, a holistic approach which involves the adoption of cultural/mechanical/biological/chemical methods is essential to keep the weeds below the threshold level for higher yield, productivity and net returns in oilseed crops.