Search Results (55)

This paper offers an exhaustive review of various pivotal aspects of forage whole-plant maize silage. It commences with an exploration of the foundational elements of planting, including the growing environment, variety selection, planting techniques, management practices, and harvesting considerations. The paper assesses the nutritional value of maize silage, its effects on animal health, and its current applications in livestock farming. Additionally, it elucidates the principles of fermentation, pathogen control, and the impact of fermentation technology on silage quality. The paper also discusses utilization strategies and technological advancements. A historical perspective is provided, alongside an analysis of current challenges, opportunities, and the global market positioning of maize silage. Furthermore, the paper delves into future prospects by addressing sustainable development strategies, adaptation to climate change, and ethical and economic controversies. The primary aim is to serve as a comprehensive reference for further research, production practices, and industrial chain development in the domain of forage whole-plant maize silage. Full article

In integrated crop–livestock systems (ICLSs), grazing intensity and crop rotation influence residue dynamics, making it essential to assess shoot and root contributions to soil carbon (C) and nitrogen (N) inputs. This study aimed to assess the shoot and root biomass of Italian ryegrass, soybean, and maize; the distribution of roots within the soil profile; and the contributions of shoot and root biomass to soil C and N under varying winter grazing intensities and summer crop rotations. The experiment was conducted within a long-term (12-year) field protocol, arranged in a randomized complete block design with split plots and four replicates. Grazing intensity was defined as the following: (i) moderate grazing—forage allowance equivalent to 2.5 times the potential dry matter intake of sheep, and (ii) low grazing—forage allowance equivalent to 5.0 times the intake potential. Grazing intensities (moderate and low) were allocated to the main plots, while cropping systems—monoculture (soybean/soybean) and crop rotation (soybean/maize)—were assigned to the subplots. Soil depth layers (0–10, 10–20, 20–30, and 30–40 cm) were treated as sub-subplots. Root samples of Italian ryegrass, soybean, and maize were collected using the soil monolith method. Low grazing intensity (8.6 Mg ha⁻¹) promoted greater aboveground biomass production of Italian ryegrass compared to moderate intensity (6.6 Mg ha⁻¹). Maize exhibited a higher capacity for both root and shoot biomass accumulation, with average increases of 85% and 120%, respectively, compared to soybean. Root biomass was primarily concentrated in the surface soil layer, with over 70% located within the top 10 cm. Italian ryegrass showed a more uniform root distribution throughout the soil profile compared to soybean and maize. Carbon inputs were higher under crop rotation (17.2 Mg ha⁻¹) than under monoculture (15.0 Mg ha⁻¹), whereas nitrogen inputs were greater in soybean monoculture (0.23 Mg ha⁻¹) than in crop rotation (0.16 Mg ha⁻¹). Low grazing intensity in winter and summer crop rotation with high-residue and quality species enhance the balance between productivity and soil C and N inputs, promoting the sustainability of ICLSs. Full article

This study aimed to provide deeper insights into fermentation dynamics, aerobic stability, and bacterial community composition during the short-term ensiling of maize forage with lactic acid bacteria-based inoculants. A 50:50 combination of Lentilactobacillus buchneri DSM2250 and Lactococcus lactis DSM11037 (LBL target application: 150,000 CFU per 1 g forage) was tested alongside an untreated control (C) over fermentation periods of 2, 4, 8, 16, and 32 days. A total of 50 3 L mini-silos were filled with 2 kg of fresh maize each and stored at 20 °C. The pH, dry matter, nutrient profiles, volatile fatty acids, lactic acid, alcohols, ammonia-N, microbiological counts (yeast and mold), and aerobic stability of all samples were analyzed after seven days of air exposure. LBL silage showed higher average dry matter content (DMc) and crude protein (CP) levels by 1.5%, p < 0.001, and 10.8%, p < 0.001, respectively, as well as reduced average dry matter (DM) losses by half (p < 0.001) compared to pure silage. The beneficial effects of inoculation became more pronounced with prolonged storage, particularly by day 32 of fermentation. LBL silage showed increased production of lactic and acetic acids by an average of 55.5% and 5.0%, respectively, (p < 0.01) and significantly reduced butyric acid formation by approximately 14 times. Ethanol and ammonia-N concentrations were also reduced by 55.4% and 25.6%, respectively (p < 0.001), while the pH value remained 0.17 units lower (p < 0.001) compared to the control. The combination of the two strains improved silage aerobic stability by 2.4 days (p < 0.001) and extended shelf life by reducing yeast counts (8.02 vs. 7.35 log₁₀CFU g⁻¹ FM, p < 0.001), while maintaining the pH value close to its initial level. Therefore, compared to the untreated control, the inoculated silage exhibited higher nutritional value, reduced fermentation losses, and suppressed undesirable microbial activity. The positive effects of inoculation became increasingly evident over time, particularly by day 32, highlighting the synergistic benefits of using mixed-strain lactic acid bacteria. These findings support the use of LBL inoculants as an effective strategy to enhance short-term silage quality and stability. Full article

Appropriate fertilization depth promotes the absorption and transport of nutrients, crop growth and yield. However, little is known about whether deep fertilization improves crude protein synthesis and how to regulate it. A two-year field experiment was conducted with various fertilization depths: (1) conventional fertilization (CF), (2) fertilization application depth at 30 cm (DF), and (3) fertilizer average application at depths of 15 cm and 30 cm (AF). The fertilization rates under all treatments were 300 kg N ha⁻¹ nitrogen fertilizer (urea, 46% N), 150 kg P₂O₅ ha⁻¹ calcium superphosphate (16% P₂O₅), and 135 kg K₂O ha⁻¹ potassium sulfate (51% K₂O). The nitrogen/potassium (N/K) ratio, the activities of nitrate reductase [NR], glutamine synthetase [GS], and glutamic pyruvic transaminase [GPT], crude protein content in leaves, stems, and grains, as well as the relationships among the parameters were explored. The result showed that deep fertilization (DF) significantly improved the N/K ratio. NR activity in DF increased by 26.30%, 35.56%, and 57.30% in leaves, stems, and grains, respectively, when compared to conventional fertilization (CF), and by 54.22%, 43.27%, and 28.44% when compared to average fertilization (AF). GS activity in DF increased by 29.67%, 47.96%, and 47.46% in leaves, stems, and grains when compared to CF, and by 40.05%, 31.51%, and 40.62% when compared to AF. GPT activity in DF was significantly higher than CF and AF in grains, and differences between treatments were significant. Crude protein content was significantly correlated with NR and GS activities in leaves, GPT activity in stems, as well as GS and GPT activities in grains. The crude protein content of leaves and grains in DF was significantly higher than in CF and AF. In conclusion, DF significantly improved crude protein synthesis and increased the crude protein content of forage maize by increasing the whole plant N/K ratio, NR and GS activities in leaves, as well as GS and GPT activities in grains. It is a highly efficient cultivation technology that significantly improves the quality of forage maize. Full article

The increasing demand for high-quality forage alternatives necessitates the exploration of novel feed resources such as giant juncao (GJ). This study evaluated the feasibility of giant juncao (GJ) as silage by analyzing its fermentation products, bacterial community, and metabolic profiles during ensiling. After the natural fermentation of giant juncao (NGJ) for 1, 3, 7, 15, 30, and 60 days, a random sampling of NGJ was conducted to analyze its chemical composition, fermentation parameters, and microbial number. Fresh, 3-day, and 60-day ensiled GJ were further analyzed via high-throughput sequencing and KEGG functional prediction. Following 60 days of ensiling, NGJ displayed acetate-type fermentation with high acetic acid and ammonia nitrogen concentrations, and low lactic acid concentration and the ratio of lactic-to-acetic acid. A microbial community analysis indicated Weissella as the predominant genus during the initial fermentation phase (3-day NGJ), whereas Lactobacillus emerged as the dominant taxonomic group in the late-stage fermentation (60-day NGJ). A comparative functional analysis revealed statistically significant divergences (p < 0.05) in KEGG pathway distributions between fresh and ensiled GJ. The ensiling process notably inhibited pathways associated with lipid synthesis, cofactor and vitamin metabolism, energy production, and amino acid utilization while concurrently enhancing carbohydrate and nucleotide metabolic activities. A nutritional evaluation confirmed GJ’s suitability as a sustainable silage maize alternative, with favorable water-soluble carbohydrate (8.57% DM) and crude protein (14.6% DM) levels. To ensure optimal preservation efficacy, the experimental findings emphasize the necessity of a minimum 30-day fermentation period for stabilizing GJ silage quality. These findings offer valuable insight into the microbial and metabolic mechanisms of high-moisture silage fermentation. Full article

Irrigation water quality is of critical importance for optimum crop yield of economically important field crops in the Kahramanmaraş plains. A preliminary ecotoxicological assessment is necessary before large-scale irrigation. Therefore, this study aims to evaluate the quality of irrigation water supplied from different water sources (Karasu, Erkenez, and Oklu streams on the Aksu River and Sır Dam) and the effects on the seed germination and early seedling growth of different field crops (wheat, alfalfa, ryegrass, and maize) irrigated with this water. For this, in order to evaluate the effects on seed germination and early growth parameters of forage crop seedlings, a Petri dish germination test was carried out with four replications using a completely randomized design (CRD). Before the germination assay, heavy metal concentrations including copper (Cu), iron (Fe), lead (Pb), chromium (Cr), arsenic (As), nickel (Ni), and cadmium (Cd) were analyzed in water samples obtained from different water sources. In all water samples used for the experiment, Cu concentrations exceeded the acceptable limit of 0.2 mg L⁻¹. The Cu levels found were 0.98 mg L⁻¹ in Karasu (KC), 1.627 mg L⁻¹ in Oklu (OC), 0.945 mg L⁻¹ in Erkenez (EC), and 1.218 mg L⁻¹ in Sır Dam (SD) waters. Additionally, Fe exceeded the limit only in KC, while Cd surpassed the permissible levels in EC and SD water samples. Seeds exposed to different water treatments were germinated in a climate chamber at 20 ± 1 °C. Over two weeks, daily germination and seedling growth parameters were measured. The results indicated that higher heavy metal concentrations in irrigation water led to a decline in seed germination rates and adversely impacted early seedling growth. Notably, water from Karasu Creek exhibited the most significant negative impact on all germination and growth parameters in the tested crops, especially due to Cu and Fe metal toxicity. Additionally, ryegrass seeds were most affected by these irrigation waters. This study highlights the importance of using uncontaminated quality irrigation water for optimal crop production by quantifying its impact, such as the percentage of decrease in germination or seedling growth. Full article

The accelerating development of the forage industry and the land resources finiteness require the high-efficient forage cropping strategies. To investigate the nutritive quality of the mixed forage crop cultivation, a three-round field test of two double-cropping systems (DCSs) based on maize (Dongdan 60 and Dongdan 1331) and sorghum (1230 and cfsh30) as the summer forage crop and alfalfa as the preceding winter forage crop were compared. This study investigated the impact on nutritive value and silage fermentation quality. The M-A system (alfalfa following a preceding crop of maize) outperformed the S-A system (alfalfa following a preceding crop of sorghum) in silage fermentation quality (by 2.81 of M and 2.22 of A), crude ash (by 0.94% of M and 3.5% of A), phosphorus content (by 0.1% of M and 0.17% of A), and potassium content (by 0.47% of M and 0.41% of A). Within the M-A, the maize Dongdan 60 (M1)–alfalfa WL525 (A) combination under the suitable sowing condition (D1) achieved the best nutritive quality exhibiting, not only the highest Flieg score (88.17 of M1 and 92.5 of A) but also the highest crude ash content (6.71% of M1 and 11.82% of A), phosphorus content (0.38% of M1 and 0.48% of A), and potassium content (1.68% of M1 and 1.55% of A). Delayed sowing reduced nutrient accumulation and altered fermentation profiles, highlighting the importance of timely sowing. The study revealed that the double-cropping rotation of maize and alfalfa is a promising strategy to optimize nutritive quality. Full article

The pH value of maize silage can accurately reflect its quality. In this study, a colorimetric sensor array (CSA) combined with hyperspectral imaging (HSI) was used to predict the pH value of maize silage during secondary fermentation. Seventeen color-sensitive dyes were used to construct the CSA, which was subsequently applied to capture the volatile odor profiles of maize silage samples. Hyperspectral images of the color-sensitive dyes on the CSA were acquired using the HSI technique. Different algorithms were used to preprocess the raw spectral data of each dye, and a partial least squares regression (PLSR) model was built for each dye separately. Subsequently, the adaptive bacterial foraging optimization (ABFO) algorithm was employed to identify three color-sensitive dyes that demonstrated heightened sensitivity to pH variations in maize silage. This study further compared the capabilities of individual dyes, as well as their combinations, in predicting the pH value of maize silage. Additionally, a novel feature wavelength extraction method based on the ABFO algorithm was proposed, which was then compared with two traditional feature extraction algorithms. These methods were combined with PLSR and backpropagation neural network (BPNN) algorithms to construct a quantitative prediction model for the pH value of maize silage. The results show that the quantitative prediction model constructed based on three dyes was more accurate than that constructed based on an individual dye. Among them, the ABFO-BPNN model constructed on the basis of combined dyes had the best prediction performance, with prediction correlation coefficient ($R_P^2$), root mean square error of the prediction set (RMSEP), and ratio of performance deviation (RPD) values of 0.9348, 0.3976, and 3.9695, respectively. The aim of this study was to develop a reliable evaluation model to achieve fast and accurate predictions of silage pH. Full article

Despite efforts to prevent atypical ensiling conditions, such as delayed ensiling or sealing, these issues frequently occur in practice. This study aimed to investigate the effects of delayed ensiling (forage held for 24 h) and sealing, along with inoculation using a blend of Lentilactobacillus buchneri and Lactococcus lactis, on the characteristics of the resulting silages. Whole-plant maize (Zea mays L.) was treated with or without a commercial inoculant and ensiled (36% dry matter) for 60 days in 3.0 L glass containers. The forage was either ensiled immediately or subjected to a 24 h delay before ensiling. During the delay, the forage was either covered or left uncovered. Each treatment was replicated five times. All data were analyzed using the MIXED procedure of SAS statistical software (version 9.4; SAS Institute Inc., Cary, NC, USA). Delaying the ensiling process by 24 h worsens fermentation parameters, significantly increases dry matter (DM) losses (p < 0.01), and significantly reduces aerobic stability and the hygienic quality of the silage (p < 0.01), as evidenced by higher concentrations of undesirable fermentation products and elevated yeast and mold counts. The inoculation has a significant impact on both forage before ensiling and the characteristics of the resulting silage. Maize forage treated with inoculant showed a lower temperature increase by 8.2–8.1 °C (p < 0.01) when delayed for 24 h before ensiling. In silages, it also resulted in a reduced pH (p < 0.01); increased concentrations of lactic acid; acetic acid; and 1,2-propanediol (p < 0.01); and decreased levels of negative fermentation indicators such as ammonia-N, alcohols, and butyric acid (p < 0.01) During both the fermentation and aerobic exposure periods, inoculated silages exhibited up to 36% and 2.6 times lower (p < 0.01) dry matter loss, while suppressing the growth of yeasts and molds by up to 2.6 and 3.1 times (p < 0.01), respectively, compared to non-inoculated silages. The results of this study support the recommendation to minimize the duration of aerobic exposure of fresh forage during silo filling and to use LAB-based inoculants. Full article

Intercropping systems and exogenous microorganism additives are recognized for their potential to influence silage fermentation and quality. This study aims to evaluate the impacts of maize–lablab bean intercropping and lactic acid bacteria (LAB) additives on silage yield, nutritional quality, and economic profitability. A randomized block design was employed with two cropping patterns—maize monocrop (M) and maize–lablab intercrop (ML)—and five additive treatments: No additives (CK), and varying ratios of Lactobacillus Plantarum (LP) and Lactobacillus Buchneri (LB), T1 (100% LP), T2 (9LP:LB), T3 (8LP:2LB), and T4 (100% LB). The silage was analyzed and evaluated for its nutritional quality, fermentation quality, and fermentation effect after 90 days of fermentation. ML intercropping significantly enhanced the fresh matter yields by 8.59% and crude protein content by 8.73% compared to M. From the point of view of inoculation with different lactobacilli, the pH, AA, and NH₃-N/TN were lower in the T2 and T3 treatments than in the other treatments, while LA was significantly higher. The V-score, which reflects the overall fermentation quality, was excellent across all treatments, with scores exceeding 80 points; the T2 treatment in ML silage achieved the highest score of 99.58. In addition, intercropping can increase the net income of farmers by 21.67%. In conclusion, maize–lablab intercropping combined with LAB inoculation, particularly with the T2 and T3 treatments, significantly enhances the silage quality and economic returns by reducing pH, increasing the LA content, and improving the CP levels. This study is the first to comprehensively analyze the synergistic effects of altering cropping systems and adding functional microorganisms on forage yield and fermentation quality, offering strategic insights for farms, especially mixed farms, to produce high-quality feed. We recommend adopting these methods to improve feed quality and maximize the profitability of silage production systems. Full article

Fungal and mycotoxin control at a primary stage in the food chain is crucial to maintaining the nutritional quality of animal feed. The control of fungal and mycotoxins is one of the essential points that a good biosecurity program must establish to ensure the safe feeding and protection of animal and human health. Acquiring a comprehensive understanding of the role of mycotoxins is vital to identifying breaches of this control and enabling the performance of proper risk assessments and accurate risk management strategies. This study focused on the identification of regulated and emerging mycotoxins in agricultural fields and dairy farms through an analytical methodology by ultra high-performance liquid chromatography coupled to tandem mass spectrometry (UHPLC-QTrap-MS/MS). This allowed us to identify a Portuguese mycotoxin profile in the maize value chain for the first time. Regarding our occurrence data, mycotoxins were identified in all samples, namely seeds, flowering plants, grain and forage at harvest, maize silage, and animal feed. FBs and ENNB were highly present in all stages of the production cycle. This work offers an initial insight into a full screening of regulated and emerging mycotoxins within an important agricultural commodity. The high occurrence of these compounds points to the need to perform occurrence surveys from an integrative perspective to protect consumers’ health, especially within food chains that provide various important staple foods worldwide. Full article

The optimal combination of the nitrogen fertilizer application and planting density with reference to the silage maize yield and quality remains unclear. We hypothesized that increasing both would increase yields following the law of diminishing returns. Yayu26, a silage maize cultivar, was used in a split-plot experiment to investigate the effects of nitrogen fertilizer and planting density on growth, nutrient characteristics, and chlorophyll fluorescence. The main plots were assigned to three planting densities: 60,000 (A1), 75,000 (A2), and 90,000 (A3) plants hm⁻², and the subplots were assigned to four nitrogen fertilizer rates: 0 (B1), 120 (B2), 240 (B3), and 360 (B4) kg hm⁻². The results showed that increasing the nitrogen application rate and planting density both enhanced silage maize yield. Nitrogen accumulation and agronomic use efficiency peaked at a planting density of 75,000 hm⁻². Structural equation modeling showed that the nitrogen application rate and planting density affected nitrogen accumulation and nutrient properties by influencing chlorophyll fluorescence parameters and nitrogen agronomic efficiency, ultimately resulting in a positive effect on the yield. The A3 × B2 treatments exhibited higher nitrogen accumulation, potentially compensating for any deficiencies in the dry-matter yield. Therefore, the A3 × B2 treatment was evaluated as the optimal treatment to achieve sustainable and economically feasible silage maize production. Full article

Conservation systems involving trees enhance the sustainability of tropical soils. However, little is known on the effect of integrated systems with native and exotic trees on soil chemical quality in the eastern Amazon. We aimed to measure changes in soil chemical quality in integrated production systems in Pindaré-Mirim, Maranhão, Brazil. This study was carried out in 2017 and 2018, evaluating (i) perennial pasture; (ii) crop–livestock–forest integration-I (CLFI-I)—eucalyptus rows interspersed with maize + Urochloa brizantha intercropping; (iii) CLFI-II—babassu palm trees (Attalea speciosa Mart.) with maize + Megathyrsus maximus intercropping; and (iv) maize + M. maximus intercropping. Soil chemical attributes at depths of 0.00–0.10 m, 0.10–0.20 m, 0.20–0.30 m, and 0.30–0.50 m, forage productivity, and soil cover were evaluated. CLFI-II promoted the highest soil organic matter concentration in topsoil and highest pH, lowest Al³⁺ levels, and potential acidity (H+Al) at all soil depths. Soil under pasture showed the highest N, K⁺, Ca²⁺ concentrations, sum of bases, and cation exchange capacity. Changes in CLFI-II are associated with the babassu palm’s ability to modulate the surrounding environment, giving the species a competitive advantage in anthropic environments. The time of adoption is crucial for improving soil fertility in the Brazilian eastern Amazon. Sustainable production systems in the region must comply with long-term management plans. Full article

This study assesses the impact of defoliation applied to three developmental stages across three cropping seasons from 2021 to 2023 on growth, yield and forage quality in maize. The experimental design included three treatments: defoliation of three expanded leaves at the 3rd–4th leaf stage (DF1), the 5th–6th expanded leaves by leaf punch (DF2) and expanding leaves with the DF2 treatment (DF3) at the 6th–7th leaf stages, compared with no defoliation (control). Over three years, the most significant decrease in dry matter (DM) yield occurred in DF1 during spring sowing, while in summer sowing, the largest reduction was in DF3, both of which were correlated with changes in the number of grains per ear. The DM yields at harvest were positively correlated with plant leaf areas at the silking stage. The digestibility of forage in in vitro DM decreased concomitantly with an increase in acid detergent fiber content, indicating a decrease in forage quality. Given the frequent severe damage observed in summer sown maize and the detrimental effects of early growth stage leaf feeding on quality and quantity of spring sown maize, the application of registered insecticides is advised to reduce pest damage to maize crops. Full article

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data. Full article