4th Global Conference on Agriculture

Pea (Pisum sativum L.) is a nutritionally important cool-season crop, has been hindered in genomic and molecular-assisted studies due to its extensive and repetitive genome sequences. Addressing this issue, whole-genome re-sequenced data (WGRS) from two parental lines, ACP 20 (Pisum sativum L.), and AWP600 (Pisum fulvum), were utilized to develop agarose gel-resolvable InDel markers. A total of 1210 loci were derived through bioinformatics analysis from resequencing and Genbank SRA data. Among these loci, 49 primer pairs, averaging 6-8 primers per chromosome were designed and validated on 31 pea genotypes, including P. sativum, P. fulvum, P. elatius, and P. abyssinicum accessions. Furthermore, the markers were also evaluated in a panel of 48-F6 pea RIL population, derived from interspecies crosses between parental lines. Remarkably, 97.96% (48 loci) produced reliable and expected amplification on parental lines. Diversity analysis revealed a total of 101 alleles for 48 InDel loci on 31 pea genotypes, with an average of 2.104 alleles per locus. The mean of polymorphic information content (PIC) was 0.281, with a Shannon diversity index (I) of 0.472. Phylogenetic and principal coordinate analysis (PCoA) distinctly grouped pea genotypes into two main clusters. Evaluation of InDel markers on the RIL population through a Chi-square (χ2) test resulted in a 1:1 segregation ratio in 33.33 % of the loci tested. On the other hand, allele distribution on the remaining loci skewed towards maternal line ACP20 indicating presence of prezygotic selection due to most likely incompatibility. Furthermore, seventeen marker loci were found primarily associated with enzymatic activities and hypothetical proteins, potentially encoding genes in pea. These newly discovered InDel markers, the first of their kind in the genus Pisum, are pivotal for future marker-assisted breeding studies in peas.

This study addresses the development of biopackaging for cocoa beans using agricultural waste, specifically cocoa waste, as a raw material. An exploratory and descriptive scientific methodology was used divided into three stages: literature review, laboratory experimentation, and life cycle analysis. The biopackages were prepared from cocoa mucilage, a material rich in sugars, by extracting polyhydroxyalkanoate and biocellulose. These biomaterials are subjected to different biotechnological techniques to improve their physical and barrier properties. The results showed that biopackaging has barrier indices against oxygen and water and mechanical strength like conventional packaging of petroleum-based plastics. Likewise, the life cycle analysis revealed that cocoa mucilage biopackaging can reduce greenhouse gas emissions, dependence on non-renewable resources, and energy consumption, thereby reducing negative environmental impacts in the cocoa value chain. In addition, they promote sustainable practices in the cocoa value chain, promote the circular economy of the product, reduce dependence on polluting non-renewable plastics derived from petroleum, and promote a culture of responsible consumption. Consequently, biopackaging derived from cocoa waste is a promising and environmentally friendly alternative that can be very useful for the food and packaging industry, although more research is required to improve the scalability of its production while promoting environmentally conscious management.

Value generation serves as a fundamental principle in agribusiness, signifying the potential for heightened profitability and market competitiveness. In the cocoa sector, value generation extends beyond mere enhancements in cocoa bean quality and yield, encompassing a holistic optimization of the supply chain from cultivation to consumer through different activities, like patent developments, which offers valuable insights for decision making along the chain. Hence, this study based on patents obtained from the Lens database, spanning data from 1899 to 2023. The aim is to map the trajectory and current phase of technological advancements within the cocoa industry across different stages of its value chain. The results reveal a predominant focus on improving processes during the post-harvest and transformation stages, while leaving behind the sowing and harvesting stages, where opportunities for enhancing cocoa quality also exist. This disparity highlights potential areas for technological innovation, particularly in seed improvement and harvesting methods, which could significantly benefit growers by providing new techniques for field application. This study not only charts the current landscape of cocoa technology but also identifies strategic opportunities for future development, aiming to enhance the overall quality and sustainability of cocoa production.

The application of hydrogels, recognized as water-absorbing hydrocolloids, is a commonly implemented approach in the agricultural sector to improve water resource control in light of rising food requirements. This study aimed to evaluate the water use efficiency of maize under different levels of water deficit using cashew gum-based hydrogels versus polyacrylamide hydrogels. The experiment was carried out in a greenhouse at the Federal University of Mato Grosso do Sul. A completely randomized experimental design was used in a 2x4x4 factorial scheme with 3 repetitions. The first factor was composed of types of hydrogels: cashew gum-based hydrogel with 5% K2HPO4 (H1) and Hydroplan – commercial hydrogel (H2); the second factor corresponded to four inputs: 0, 60, 120, and 240 mg pot-1, corresponding to 7.5, 15, 30, and 60 kg ha-1; while the last factor was four irrigation levels: 0, 25, 50, and 100% of water storage capacity. At 30 days after maize germination, gas exchange was analyzed using a infrared gas analyzer. Physiological variables evaluated were: net photosynthesis (A) and transpiration (E). From this data, water use efficiency (WUE) was quantified by the A/E. Our findings reveals that WUE is significantly influenced by the interaction between types x inputs of hydrogels, but there is no interaction between types x inputs x irrigation levels. Using cashew gum hydrogel is similar to using commercial hydrogels in terms of water use efficiency. Hydrogels, originating from natural substances and utilizing renewable resources in their manufacturing process, contribute to the sustainability of food production.

Hydrogels represent a strategy to improve water management in response to increasing food demands. While commercial polyacrylamide (PAM)-based hydrogels have dominated the market, cashew gum has emerged as a promising material in the context of sustainable agriculture. The objective was to evaluate the dry mass (DM) of maize under water deficit using cashew gum-based and polyacrylamide hydrogels. An experiment was carried out in a greenhouse using a completely randomized design in 2x4x4 factorial scheme with 3 repetitions. Treatments consisted of two types of hydrogels: cashew gum-based hydrogel with 5% K2HPO4 (H1) and Hydroplan – EB commercial hydrogel (H2); four hydrogel inputs: 0, 60, 120, and 240 mg pot-1, corresponding to 7.5, 15, 30, and 60 kg ha-1; and four irrigation levels: 0, 25, 50, and 100% of water storage capacity. DM was determined by oven drying the plants harvested 70 days after germination. DM was influenced by types x inputs x irrigation interaction. H1 showed the highest mean (60.85 g pot-1 and 49.35 g pot-1) under irrigation levels of 25% and 100% and inputs of 60 mg pot-1 and 120 mg pot-1, respectively, when compared to H2. H2 showed higher mean DM, 51.74 g pot-1 and 74.76 g pot-1, when compared to H1 at 100% irrigation level and at inputs of 240 mg and 60 mg pot-1, respectively. Using cashew gum hydrogel is equivalent to commercial hydrogel for improving DM at 75% and 50% water deficit. Cashew gum is a sustainable alternative for growing maize under water deficit.

Hydrogels are hydrocolloids with water-absorbent properties and can be a strategy in agriculture to enhance water management. Our objective was to determine the carboxylation efficiency of maize submitted to water deficit using cashew gum and polyacrylamide hydrogels. A greenhouse trial was carried out using a completely randomized design in 2x4x4 factorial scheme with 3 replicates. Two types of hydrogels: cashew gum with 5% K2HPO4 (H1) and Hydroplan – EB commercial hydrogel (H2); four hydrogel inputs: 0, 60, 120, and 240 mg pot-1, equivalent to 7.5, 15, 30, and 60 kg ha-1; and four irrigation levels: 0, 25, 50, and 100% of water storage capacity were assessed. At 30 days after germination, photosynthesis (A, µmol CO2 m-2 s-1), internal CO2 concentration (Ci, µmol CO2 mol-1), transpiration (E, mmol H2O m-2 s-1), stomatal conductance (gs, mol H2O m-2 s-1) were measured using portable photosynthesis equipment (Infrared Gas Analyzer - IRGA). Instantaneous carboxylation efficiency (ICE) [(μmol m-2 s-1) (μmol mol-1) -1] was quantified by A/Ci ratio. ICE is influenced by the hydrogel types x irrigation levels interaction, but no hydrogel types x hydrogel inputs x irrigation levels interaction was found. H1 showed higher ICE means (0.13 μmol m-2 s-1 /(μmol mol-1) -1), but did not differ from H2 (0.12 μmol m-2 s-1 /(μmol mol-1) -1) at 50% irrigation, respectively. H2 showed lower ICE means, 0.09 μmol m-2 s-1 /(μmol mol-1) -1, but did not differ from H1, with 0.11 μmol m-2 s-1/(μmol mol-1)-1 at 100% irrigation, respectively.

This study aimed to accuracy of predicting flavonoids in maize plants subjected to different nitrogen rates using hyperspectral reflectance and machine learning (ML) algorithms. The experiment was carried out in randomized blocks in a 4 x 5 factorial design (four N inputs: 0; 30; 60 and 120% of the recommended N input; and five readings of the reflectance spectra in maize leaves from different vegetative stages: V6, V8, V10, V12 and V14, in four replications. N inputs were applied in the V4 and V8 phenological stages, using urea as the N source. For hyperspectral analysis, four leaves from each treatment were collected and analyzed using a spectroradiometer, capturing the spectrum in the 350 to 2500 nm range. Subsequently, the leaf samples used in the reflectance readings were dried and analyzed by ultra-performance liquid chromatography in three repetitions, quantifying daidzein 1 (D1), daidzein 2 (D2), genistein 1 (G1) and genistein 2 (G2). Data obtained was subjected to machine learning analysis, testing two data set input configurations: wavelengths (WL) and calculated spectral bands, and D1, D2, G1, G2 and total isoflavones as output variables. The ML algorithms tested were artificial neural networks, REPTree, M5P decision tree, ZeroR, Random Forest and support vector machine (SVM), evaluated according to their performance by the correlation coefficient and mean absolute error. The results show that the SVM algorithm had the highest accuracy in predicting the variables D1, D2, G1, G2 and total isoflavones, outperforming the other algorithms when WL was used as input in dataset.

Existing studies on the mechanical properties of apples often lack the comprehensive data needed for accurate finite element method (FEM) model construction and validation. Moreover, many rely on store-bought apples, which are unsuitable for industrial purposes due to the treatments they undergo before reaching consumers. This study introduces a novel method for developing 3D discrete models of apple tissue at various ripening stages using FEM, focusing on the new Polish apple variety, Chopin. Detailed mechanical property measurements of the apple flesh and skin were conducted, along with physicochemical analyses. For model validation, force-displacement curves and pressure-force relationships were examined, and micro-computed tomography was used to determine bruise volumes in fruits subjected to 20%, 50%, and 80% of the destructive force. The results indicate that, depending on the application, apple flesh can be modeled as either an elastic or elastoplastic material. Consequently, parameters were established for numerical models at different stages of developmental maturity, ripening, and senescence maturity. Model validation, based on displacement-load relationships and changes in contact area under loading force, showed that elastic models achieved a minimum of 82% compliance with empirical data, while elastoplastic models exhibited at least 91% compliance. In the final phase, a method was developed to determine the stresses causing damage to apple flesh by integrating microtomographic images of damaged fruits with von Mises stress images from discrete models. This involved compression tests on fruits at 20%, 50%, and 80% of their destructive force capacity.