The agricultural sector of Colombia supports the national economy and food security due to the rich lands for cultivation. Although Colombia has a vast hydrological basin, climate change can impact agricultural productivity, generating economic and social adverse effects. For this, we evaluated the impact of some environmental variables on the production of the most sold crops using production, climatic, and hydrological data of the 1121 municipalities from 2007 to 2020. We modeled the production of coffee, rice, palm oil, sugarcane, and corn, adopting a Bayesian spatio-temporal model that involved a set of environmental variables: average temperature, minimum temperature, maximum temperature, evapotranspiration, precipitation, runoff, soil moisture, vapor pressure, radiation, and wind speed. We found that increases in the average temperatures can affect coffee (-0.2% per °C), rice (-3.76% per °C), and sugarcane (-0.19% per °C) production, meanwhile, these increases can boost palm oil (+2.55% per °C) and corn (+1.28% per °C) production in Colombia. This statement implies that the agricultural sector needs to substitute land use, promoting the production of palm oil and corn. Although our results did not find a significant effect of hydrological variables in any crop, suggesting that the abundance of water in Colombia might balance the impact of these variables. The increases in vapor pressure impact all the crops negatively (between -11.2% to -0.43% per kPa), except rice, evidencing that dry air conditions affect agricultural production. Colombia must manage the production location of the traditional products and implement agro-industrial technologies to avoid the climate change impact on crops.

Soil salinization poses a great threat to global agricultural ecosystems, and finding ways to improve the soils affected by salt and maintain soil health and sustainable productivity has become a major challenge. Various physical, chemical and biological approaches are being evaluated to address this escalating environmental issue. Among them, fully utilizing salt-tolerant plant growth-promoting bacteria (PGPB) has been labeled as a potential strategy to alleviate salt stress, since they can not only adapt well to saline soil environments but also enhance soil fertility and plant development under saline conditions. In the last few years, an increasing number of salt-tolerant PGPB have been excavated from specific ecological niches, and various mechanisms mediated by such bacterial strains, including but not limited to siderophore production, nitrogen fixation, enhanced nutrient availability, and phytohormone modulation, have been intensively studied to develop microbial inoculants in agriculture. This review outlines the positive impacts and growth-promoting mechanisms of a variety of salt-tolerant PGPB and opens up new avenues to commercialize cultivable microbes and reduce the detrimental impacts of salt stress on plant growth. Furthermore, considering the practical limitations of salt-tolerant PGPB in the implementation and potential integration of advanced biological techniques in salt-tolerant PGPB to enhance their effectiveness in promoting sustainable agriculture under salt stress are also accentuated.

Due to low adoption and sub-optimal fertilizer use and planting density recommendation in maize, redesigning and testing these technologies are required. The study was conducted to evaluate redesigned fertilizer use of maize in two pant densities (32,443 and 53,333 plants ha-1 in Central Rift Valley (CRV); 27724 and 62,000 plants ha-1 in Jimma) on farmers\' fields in contrasting agro-ecologies of Ethiopia. The on-farm study was conducted in the 2017 and 2018 cropping seasons with 3 × 2 fertilizer and plant density, factors in both regions of Ethiopia. In redesigned fertilizer use, nutrients were estimated based on the target yield. In this study, 40.8, 0.0, and 12.2 kg ha-1 N, P, and K were estimated for the redesigned fertilizer use in CRV (50% of water-limited potential yield (Yw) = 3.1 t ha-1) whereas in Jimma (50% of Yw = 7.5 t ha-1) 149.8, 9, 130.6 kg ha-1 N, P and K were estimated to produce the 50% of Yw. Linear mixed modeling was used to assess the effect of fertilizer-plant density treatments on maize yield and nutrient use efficiency. The result revealed that the average estimated maize yield for WOF, FFU, and RDFU fertilizer treatments were 2.6, 3.6, and 4.5 t ha-1 under current plant density (32,443 plants ha-1) in CRV whereas the average yields of these treatments were 3.2, 4.5 and 4.5 t ha-1 respectively when maize was grown with redesigned plant density (53,333 plants ha-1) in the same location. The average maize yield with WOF, FFU, and RDFU were 3.0, 4.6, and 4.6 t ha-1 with 27,774 plants ha-1 plant density in Jimma whereas the average maize yields over the two seasons with the same treatments were 4.3, 6.0 and 8.0 t ha-1 respectively when the crop is planted with 62,000 plants ha-1 plant density. The RDFU and redesigned plant density resulted in significantly higher yield compared to their respective control CRV but RDFU significantly increased maize yield when it was planted at redesigned (62,000 plant ha-1) in Jimma. FFU and RDFU were economically viable and redesigned plant density was also a cheaper means of improving maize productivity, especially in the Jimma region. Soil organic carbon and N were closely related to the grain yield response of maize compared to other soil factors. In conclusion, this investigation gives an insight into the importance of redesigned fertilizer use and redesigned plant density for improving maize productivity and thereby narrowing the yield gaps of the crop in high maize potential regions in Ethiopia like Jimma.

CONCLUSIONS: Millets\' protein studies are lagging behind those of major cereals. Current status and future insights into the investigation of millet proteins are discussed. Millets are important small-seeded cereals majorly grown and consumed by people in Asia and Africa and are considered crops of future food security. Although millets possess excellent climate resilience and nutrient supplementation properties, their research advancements have been lagging behind major cereals. Although considerable genomic resources have been developed in recent years, research on millet proteins and proteomes is currently limited, highlighting a need for further investigation in this area. This review provides the current status of protein research in millets and provides insights to understand protein responses for climate resilience and nutrient supplementation in millets. The reference proteome data is available for sorghum, foxtail millet, and proso millet to date; other millets, such as pearl millet, finger millet, barnyard millet, kodo millet, tef, and browntop millet, do not have any reference proteome data. Many studies were reported on stress-responsive protein identification in foxtail millet, with most studies on the identification of proteins under drought-stress conditions. Pearl millet has a few reports on protein identification under drought and saline stress. Finger millet is the only other millet to have a report on stress-responsive (drought) protein identification in the leaf. For protein localization studies, foxtail millet has a few reports. Sorghum has the highest number of 40 experimentally proven crystal structures, and other millets have fewer or no experimentally proven structures. Further proteomics studies will help dissect the specific proteins involved in climate resilience and nutrient supplementation and aid in breeding better crops to conserve food security.

BACKGROUND: Oil bodies or lipid droplets (LDs) in the cytosol are the subcellular storage compartments of seeds and the sites of lipid metabolism providing energy to the germinating seeds. Major LD-associated proteins are lipoxygenases, phospholipaseD, oleosins, TAG-lipases, steroleosins, caleosins and SEIPINs; involved in facilitating germination and enhancing peroxidation resulting in off-flavours. However, how natural selection is balancing contradictory processes in lipid-rich seeds remains evasive. The present study was aimed at the prediction of selection signatures among orthologous clades in major oilseeds and the correlation of selection effect with gene expression.
RESULTS: The LD-associated genes from the major oil-bearing crops were analyzed to predict natural selection signatures in phylogenetically close-knit ortholog clusters to understand adaptive evolution. Positive selection was the major force driving the evolution and diversification of orthologs in a lineage-specific manner. Significant positive selection effects were found in 94 genes particularly in oleosin and TAG-lipases, purifying with excess of non-synonymous substitution in 44 genes while 35 genes were neutral to selection effects. No significant selection impact was noticed in Brassicaceae as against LOX genes of oil palm. A heavy load of deleterious mutations affecting selection signatures was detected in T-lineage oleosins and LOX genes of Arachis hypogaea. The T-lineage oleosin genes were involved in mainly anther, tapetum and anther wall morphogenesis. In Ricinus communis and Sesamum indicum > 85% of PLD genes were under selection whereas selection pressures were low in Brassica juncea and Helianthus annuus. Steroleosin, caleosin and SEIPINs with large roles in lipid droplet organization expressed mostly in seeds and were under considerable positive selection pressures. Expression divergence was evident among paralogs and homeologs with one gene attaining functional superiority compared to the other. The LOX gene Glyma.13g347500 associated with off-flavor was not expressed during germination, rather its paralog Glyma.13g347600 showed expression in Glycine max. PLD-α genes were expressed on all the tissues except the seed,δ genes in seed and meristem while β and γ genes expressed in the leaf.
CONCLUSIONS: The genes involved in seed germination and lipid metabolism were under strong positive selection, although species differences were discernable. The present study identifies suitable candidate genes enhancing seed oil content and germination wherein directional selection can become more fruitful.

Soybean is an essential crop to fight global food insecurity and is of great economic importance around the world. Along with genetic improvements aimed at boosting yield, soybean seed composition also changed. Since conditions during crop growth and development influences nutrient accumulation in soybean seeds, remote sensing offers a unique opportunity to estimate seed traits from the standing crops. Capturing phenological developments that influence seed composition requires frequent satellite observations at higher spatial and spectral resolutions. This study introduces a novel spectral fusion technique called multiheaded kernel-based spectral fusion (MKSF) that combines the higher spatial resolution of PlanetScope (PS) and spectral bands from Sentinel 2 (S2) satellites. The study also focuses on using the additional spectral bands and different statistical machine learning models to estimate seed traits, e.g., protein, oil, sucrose, starch, ash, fiber, and yield. The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. Our results indicate that VNIR3 prediction performance was the highest followed by VNIR2, VNIR1, SWIR1, and SWIR2. Among the seed traits, sucrose yielded the highest predictive performance with RFR model. Finally, the feature importance analysis revealed the importance of MKSF-generated vegetation indices from fused images.

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Remote sensing has been increasingly used in precision agriculture. Buoyed by the developments in the miniaturization of sensors and platforms, contemporary remote sensing offers data at resolutions finer enough to respond to within-farm variations. LiDAR point cloud, offers features amenable to modelling structural parameters of crops. Early prediction of crop growth parameters helps farmers and other stakeholders dynamically manage farming activities. The objective of this work is the development and application of a deep learning framework to predict plant-level crop height and crown area at different growth stages for vegetable crops. LiDAR point clouds were acquired using a terrestrial laser scanner on five dates during the growth cycles of tomato, eggplant and cabbage on the experimental research farms of the University of Agricultural Sciences, Bengaluru, India. We implemented a hybrid deep learning framework combining distinct features of long-term short memory (LSTM) and Gated Recurrent Unit (GRU) for the predictions of plant height and crown area. The predictions are validated with reference ground truth measurements. These predictions were validated against ground truth measurements. The findings demonstrate that plant-level structural parameters can be predicted well ahead of crop growth stages with around 80% accuracy. Notably, the LSTM and the GRU models exhibited limitations in capturing variations in structural parameters. Conversely, the hybrid model offered significantly improved predictions, particularly for crown area, with error rates for height prediction ranging from 5 to 12%, with deviations exhibiting a more balanced distribution between overestimation and underestimation This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications. However, the prediction quality is relatively low at the advanced growth stage, closer to the harvest. In contrast, the prediction quality is stable across the three different crops. The results indicate the presence of a robust relationship between the features of the LiDAR point cloud and the auto-feature map of the deep learning methods adapted for plant-level crop structural characterization. This approach effectively captured the inherent temporal growth pattern of the crops, highlighting the potential of deep learning for precision agriculture applications.

Drought stress following climate change is likely a scenario that will have to face crop growers in tropical regions. In mitigating this constraint, the best option should be the selection and use of resilient varieties that can withstand drought threats. Therefore, a pot experiment was conducted under greenhouse conditions at the Research and Teaching Farm of the Faculty of Agronomy and Agricultural Sciences of the University of Dschang. The objectives are to identify sensitive growth stage, to identify drought-tolerant genotypes with the help of yield-based selection indices and to identify suitable selection indices that are associated with yield under non-stress and stress circumstances. Eighty-eight cowpea genotypes from the sahelian and western regions of Cameroon were subjected to drought stress at vegetative (VDS) and flowering (FDS) stages by withholding water for 28 days, using a split plot design with two factors and three replications. Seed yields under stress (Ys) and non-stress (Yp) conditions were recorded. Fifteen drought indices were calculated for the two drought stress levels against the yield from non-stress plants. Drought Intensity Index (DII) under VDS and FDS were 0.71 and 0.84 respectively, indicating severe drought stress for both stages. However, flowering stage was significantly more sensitive to drought stress compared to vegetative stage. Based on PCA and correlation analysis, Stress Tolerance Index (STI), Relative Efficiency Index (REI), Geometric Mean Productivity (GMP), Mean Productivity (MP), Yield Index (YI) and Harmonic Mean (HM) correlated strongly with yield under stress and non-stress conditions and are therefore suitable to discriminate high-yielding and tolerant genotypes under both stress and non-stress conditions. Either under VDS and FDS, CP-016 exhibited an outstanding performance under drought stress and was revealed as the most drought tolerant genotype as shown by ranking, PCA and cluster analysis. Taking into account all indices, the top five genotypes namely CP-016, CP-021, MTA-22, CP-056 and CP-060 were identified as the most drought-tolerant genotypes under VDS. For stress activated at flowering stage (FDS), CP-016, CP-056, CP-021, CP-028 and MTA-22 were the top five most drought-tolerant genotypes. Several genotypes with insignificant Ys and irrelevant rank among which CP-037, NDT-001, CP-036, CP-034, NDT-002, CP-031, NDT-011 were identified as highly drought sensitive with low yield stability. This study identified the most sensitive stage and drought tolerant genotypes that are proposed for genetic improvement of cowpea.

The utilization of various agrochemicals in crop production technology leads to soil health and fertility depletion. Multiple measures have been taken to revitalize the health of polluted soil. In this context, organic agriculture has increased over the past few years to overcome the detrimental effects of extensive modern agricultural practices. Several traditional organic formulations, such as panchagavya, jeevamurtha, beejamurtha, bokashi, etc., are vital in converting polluted farmlands into organic. Various countries have their own organic formulations to improve crop growth and yield. These formulations are rich sources of many macro and micronutrients, growth-promoting phytohormones, and provide resistance against biotic and abiotic stresses. Apart from these benefits, these formulations consist of several groups of beneficial microorganisms that belong to the phyla Proteobacteria, Firmicutes, Bacteroides, and Actinobacteria, while some of the novel groups of microorganisms were also reported from the ingredients used in the preparation of these organic formulations. These microorganisms can solubilize nutrients such as phosphorous and zinc, oxidize sulfur, reduce nitrate, and are also involved in the production of indole acetic acid, ethylene reduction enzyme (1-aminocyclopropane-1-carboxylic acid deaminase), and organic acids that promote plant growth and induce resistance in the plant system. Hence, the utilization of traditional organic formulations helps in the reclamation of environmental health without compromising crop yields. This review describes the importance of organic farming, the preparation and application of different types of traditional organic formulations in different countries, and the microbial composition and mechanism of growth promotion of different traditional organic formulations.