Dry or wet? What is the best choice to determine gonadosomatic and hepatosomatic indexes in females of Macrobrachium amazonicum?

The gonadosomatic (GSI) and hepatosomatic (HSI) indexes are widely used for the evaluation of reproductive biology in crustaceans. However, the use of either wet or dry weight for estimating these indexes is not standardized in the literature. Here, we compare the GSI and HSI indexes based on wet and dry samples of females of Macrobrachium amazonicum from populations with different phenotypes (large and small‐size prawns) as a model. In our analysis, for GSI, the application of wet weight tended to overestimate the values recorded in the initial (I‐II) and intermediate (III) stages of ovarian development, and underestimate the results in the final (IV‐V) stages when compared to the use of dry weight. This could be attributed to the reduction in cytoplasmic organelles and water content of the cytosol due to the increase in yolk and lipids in the oocytes throughout ovarian development. For HSI, no significant differences were detected between wet and dry samples. However, the dry HSI computed at all stages of development was overestimated when compared to the use of wet samples, and only the hepatopancreas of females at the final stage (V) showed a significant decrease in water content. Thus, our results indicate that M. amazonicum females maintain constant hepatopancreatic reserves throughout ovarian development. In this way, the use of dry weight is more adequate to estimate GSI and HSI in this species.     

Marine and coastal protected and conserved areas strategy in Brazil: Context,lessons, challenges,finance, participation,new management models,and first results

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Fish invading deserts: non‐native species in arid Moroccan rivers

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Effects of artificial substrate and night‐time aeration on the water quality in Macrobrachium amazonicum (Heller 1862) pond culture

The effects of artificial substrate and night‐time aeration on the culture of Macrobrachium amazonicum were evaluated in 12 ponds stocked with 45 prawns m^?2. A completely randomized design in 2 × 2 factorial scheme with three replicates was used. The combination of factors resulted in four treatments: with substrate and aeration (SA), with substrate and without aeration (SWA), without substrate and with aeration (WSA) and without substrate and aeration (WSWA). The presence of substrate in SA and SWA treatments reduced suspended particles (seston) by ~17.3% and P‐orthophosphate by ~50%. The use of aerator (WSA and SA treatments) significantly (P < 0.05) increased the concentration of dissolved oxygen, suspended particles and nutrients in the pond water. These results indicate that the effect of substrate on turbidity and total suspended solids (TSS) values is opposite to the effect of the aerator. The aerators in semi‐intensive grow‐out M. amazonicum farming lower water quality because they increased the amount of detritus and nutrients in the pond water. On the other hand, the use of artificial substrate reduces turbidity values, chlorophyll a, TSS and P‐orthophosphate concentrations. Therefore, the combination of substrate addition and night‐time aeration is not interesting because they have opposite effects.     

Nitrogen Fertilizers and NH3 Volatilization: Effect of Temperature and Soil Moisture

ABSTRACT

The volatilization of ammonia is the main reaction that decreases the efficiency of nitrogen fertilization and in order to reduce losses. new technologies such as addition of N-n-butyltriamide thiophosphate (NBPT) to the conventional urea granule (UNBPT) or the covering with polymer and sulfur (UPS) have been developed with the aim to optimize nitrogen fertilization. This work aimed to evaluate the volatilization of ammonia (NH₃) in conventional urea (CU) and fertilizers with associated technology under: (a) three temperature conditions (b) and three soil moisture management. The fertilizer CU presented the highest losses by volatilization of 25.93 mg dm^?3 while fertilizers with associated technology registered 23.93 mg dm^?3 and 8.26 mg dm^?3 for UNBPT and UPS. respectively. The highest volatilization of NH₃ was registered at 45°C for all fertilizers. Fertilizers with associated technology extended the N-release time. delaying the volatilization peak up to the 6^th day or even promoted the gradual release of fertilizer in the soil. such as UPS. The UNBPT showed the lowest volatilization values in the 1^st water application. while the CU had lower volatilization values at 25°C (14.48 mg dm^?3 NH₃) and 35°C (16.99 mg dm^?3 NH₃) when the matric potential was increased from ?100 to ?50 kPa in the 1^st application of water. The UPS did not differ from the volatilization values for the three times of water application.     

A genome‐wide association study for partial resistance to southern corn rust in tropical maize

Southern corn rust (SCR) is a fungal disease found on corn in several countries worldwide. In Brazil, the disease can result in productivity losses of 65%, especially in areas with a history of the disease. In this study, the genetic architecture and identification of genomic regions associated with SCR resistance was investigated by performing a genome‐wide association study. Genotyping‐by‐sequencing was performed to carry out the association between single nucleotide polymorphism (SNP) markers and phenotypic data from two environments on a panel of 164 maize inbred lines. Eight SNPs were identified as significant for SCR resistance. These SNPs were colocalized with QTL regions, some of which underlie candidate resistance genes with functions that play an important role in the stress response during pathogen recognition. These candidate genes, involved in plant defense pathways, could be associated with partial resistance to SCR and provide a partial comprehensive insight into the genetic architecture of this trait. After validation of the SNPs, they will be useful for marker–assisted selection and for a better understanding of maize resistance to SCR.     

Landscape and local correlates with anuran taxonomic,functional and phylogenetic diversity in rice crops

Context

Deforestation is a major driver of biodiversity loss, mainly due to agriculture. As rice is among the world’s most important crops, determining how agricultural communities are shaped is imperative. However, few studies have addressed the factors that alter community assembly in human-modified landscapes. We aim to quantify taxonomic, functional, trait and phylogenetic diversity of an anuran community from rice crops on a biodiversity hotspot.

Objectives

Identify local and landscape characteristics responsible for variations in multiple dimensions of anuran diversity in rice crops.

Methods

This study was performed in Tocantins, Brazil. We chose 36 lentic waterbodies on rice fields for anuran sampling. We quantified taxonomic diversity (TD), functional diversity (FD) and phylogenetic diversity (PD) for each waterbody. We also estimated the mean functional differences among species for each trait separately. To evaluate how local and landscape scale features affect anurans, we performed generalized linear mixed models in 500, 1000 and 1500 m buffers around each waterbody.

Results

We found increased PD and FD in waterbodies closer to many other waterbodies and large forest patches. Anuran biomass decreased with increasing distance to the closest waterbody. Trait diversity varied with waterbody abundance and closeness, percentage of bare ground and marginal vegetation.

Conclusions

Our study emphasizes the importance of waterbody and forest patch networks for maintaining high anuran FD and PD in agricultural landscapes. As both metrics are known to be related to ecosystem resilience, understanding these patterns is pivotal for biodiversity management, especially in the tropics, where agricultural expansion is unrelenting and biodiversity is especially unique.

     

Implementing genomic selection in sour passion fruit population

Sour passion fruit is an economically important tropical fruit crop with little explored genetic potential. This study aimed to provide breeders with essential estimates of genomic breeding values in economically important traits in passion fruit, using Bayesian models which may contribute to the implementation of Genomic Selection and develop new strategies for the continuity of sour passion fruit breeding programs. For this, the following Bayesian models were tested using 183 polymorphic marks: Bayesian Ridge regression, Bayes A, Bayes B, Bayes B2, Bayes Cπ and Bayesian Lasso for estimation of genomic breeding values. To achieve this, ninety-five full-sib progenies derived from the third cycle of recurrent selection of the sour passion fruit (Passiflora edulis Sims.) at Universidade Estadual do Norte Fluminense Darcy Ribeiro—UENF were used and eight fruit yield (number of fruit, total yield, mean fruit weight, fruit length, fruit width) and quality(percent pulp, skin thickness, soluble solids) traits were assessed. The Bayes Cπ (smaller deviance information criterion) yield the best genetic predictions for almost all traits. Genetic correlations in this study indicate that the number of fruit can be used as a proxy for yield. The values of genomic heritability obtained were high and ranged from 0.62 to 0.76 and predict accuracy ranged from 0.55 to 0.75, so we can to speculate that the use of two replicates in the present study was an adequate amount to obtain phenotypic mean, which was used to adjust the genomic prediction model.     

Quantification of methane emitted by ruminants: a review of methods

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH₄), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH₄ emissions. This review documents existing methods and methodologies to measure and estimate CH₄ emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH₄ have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH₄ for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH₄ from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH₄ emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH₄ emissions from manure depends on the type of storage, animal housing, CH₄ concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH₄ emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH₄ in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.     

Nitrogen fertilization and potassium requirement for cereal crops under a continuous no-till system

Nitrogen (N) and potassium (K) are the most required nutrients for corn and wheat production. Increasing the N application rate usually boosts crop yields. However, many uncertainties remain for K management. Potassium deficiency results in yield losses, but K application rate based on the percentage of K⁺ in the cation exchange capacity (CEC) is doubtful, especially in soil with high CEC. A field trial was conducted to examine the effects of KCl application before sowing corn and wheat, by raising the percentage of K⁺ in CEC at pH 7.0 (CEC_pH7.0) to approximately 2.5%, 3.5%, and 4.5%, and adding N as a topdressing (75, 150, and 225 kg ha^-1 to corn and 40, 80, and 120 kg ha^-1 to wheat) on the nutrition and yield of corn and wheat under a continuous no-till system (30 years). Exchangeable K⁺ content increased in the topsoil (0-20 cm depth) up to 7.2 mmolc dm-3 after K application at the highest rate, which, however, did not result in significant increases in nutrient uptake and yields for both corn and wheat. The N application rate positively affected the uptake and removal of all macronutrients by corn and wheat. Applying N as a topdressing increased yields of corn and wheat by up to 83% and 22%, respectively. Our results suggest that in the soil with a high CEC_pH7.0 (162.1 mmolc dm-3), the recommendation for K application made by considering the percentage of K⁺ in the CEC_pH7.0 may result in excessive application of K fertilizer to crops with high K-recycling potential grown under a continuous no-till system.