Food and Energy Security最新文献

Agriculture is directly related to food security as it determines the global food supply. Research in agriculture to predict crop productivity and losses helps avoid high food demand with little supply and price spikes. Here, we review ten crop models and one intercomparison project used for simulating crop growth and productivity under various impacts from soil–crop–atmosphere interactions. The review outlines food security and production assessments using numerical models for maize, wheat, and rice production. A summary of reviewed studies shows the following: (1) model ensembles provide smaller modeling errors compared to single models, (2) single models show better results when coupled with other types of models, (3) the ten reviewed crop models had improvements over the years and can accurately predict crop growth and yield for most of the locations, management conditions, and genotypes tested, (4) APSIM and DSSAT are fast and reliable in assessing broader output variables, (5) AquaCrop is indicated to investigate water footprint, quality and use efficiency in rainfed and irrigated systems, (6) all models assess nitrogen dynamics and use efficiency efficiently, excluding AquaCrop and WOFOST, (7) JULES specifies in evaluating food security vulnerability, (8) ORYZA is the main crop model used to evaluate paddy rice production, (9) grain filling is usually assessed with APSIM, DAISY, and DSSAT, and (10) the ten crop models can be used as tools to evaluate food production, availability, and security.

Early and accurate prediction and simulation of grain crop yield can help maximize the revision and development of regional food policy, which is crucial for ensuring national food security. The development of unmanned aerial vehicle (UAV) technology is gradually gaining an advantage over satellite remote sensing at the field scale. In this study, we predicted maize yield using canopy vegetation indices (VIs) and crop phenology metrics obtained through UAV with ordinary least squares (OLS), stepwise multiple linear regression (SMLR) and gradient-boosted regression tree (GBRT). The results reveal that the VIs extracted from UAV imagery had a high correlation with yield (R = 0.92), facilitating crop yield estimation. Additionally, coupling crop phenology significantly improved the prediction accuracy of SMLR, with the highest R² and lowest RMSE of 0.894, 1.238 × 10³ kg ha⁻¹, respectively. But, the enhancement of GBRT by this method was slender. Its simulation outperformed OLS and SMLR with dramatic R², RMSE, and MAE of 0.892, 1.189 × 10³ kg ha⁻¹, and 9.150 × 10² kg ha⁻¹, respectively. Moreover, the blister stage was deemed the optimal stage for maize yield prediction with an accuracy rate exceeding 81%. These demonstrated the feasibility of using UAV images to predict crop yields, providing an important reference at the field scale.

Biofertilizers has potential to improve crop productivity in a sustainable manner yet their impact on forage crops yields and forage quality indices have not been extensively explored. In this study, the impact of different cropping patterns and fertilizer sources on the forage yield and quality of ajowan (Carum copticum L.), fenugreek (Trigonella foenum-graecum L.), and pea (Pisum sativum L.) was investigated. The findings shed light on the importance of biofertilizer application as a sustainable solution for enhancing forage production and quality in agricultural systems. The experimental treatments involved six cropping patterns [ajowan monoculture, fenugreek monoculture, pea monoculture, intercropping of one row ajowan + one row fenugreek + one row pea (1:1:1), two rows ajowan + two rows fenugreek + two rows pea (2:2:2), and three rows ajowan + three rows fenugreek + three rows pea (3:3:3)] and five fertilizer sources [no fertilizer (control), 100% chemical fertilizer (CF), chemical fertilizer + humic acid (CF + HU), chemical fertilizer + biofertilizer (CF + BF), and humic acid + biofertilizer (HU + BF)]. The results revealed that the 2:2:2 intercrop fertilized with CF + BF had the highest N, P, and K concentrations for the three species. The monoculture systems with CF + BF produced the highest forage yields for ajowan (5492 kg/ha), fenugreek (3811 kg/ha), and pea (12,695 kg/ha). In addition, 2:2:2 intercrop fertilized with CF + BF produced the highest forage quality for all three species, decreasing the crude fiber, acid detergent fiber, and neutral detergent fiber contents and increasing forage ASH, dry matter intake, water-soluble carbohydrates, and relative feed value. The 2:2:2 intercrop ratio combined with CF + BF application could be recommended to farmers as an eco-friendly strategy (decreasing chemical fertilizer use) for improving forage productivity and quality in sustainable agricultural systems.

Asparagine (Asn) and nitric oxide (NO) can enhance plant tolerance to abiotic stress, but their interaction is not well understood. Cotton is a vital resource for the textile industry, but its yield is reduced by drought stress, which could threaten its global supply in a warming and stressful world. Improving cotton's drought tolerance is crucial for supporting the textile industry. Two simultaneous field experiments were conducted to study the effect of Asn and sodium nitroprusside (SNP), a NO donor, on cotton's drought tolerance. Two irrigation treatments were applied: control (C: 100% A pan) and drought stress (50% A pan). The plants were also sprayed with two plant stimulants before imposing drought stress: Mock control, 20 mM Asn, and 0.2 mM SNP, either alone or together. Drought stress impaired plant growth, photosynthesis, yield, nitrogen metabolism, and antioxidant defense, while increasing oxidative stress and free amino acid levels. However, Asn and SNP treatments alleviated these negative effects and improved antioxidant enzyme activity, plant growth, yield, and nitrogen content. The Asn + SNP treatment also increased irrigation water productivity under water-limited conditions, suggesting its potential for enhancing water use efficiency in cotton production. The combined treatment was more effective than the single treatments, indicating a synergistic effect of Asn and SNP in enhancing drought tolerance in cotton. These results imply that Asn and SNP could be useful tools for sustaining cotton production under drought conditions by boosting nitrogen metabolism and antioxidant defense, thereby contributing to the global supply of cotton and supporting the textile industry.

De novo domestication—the modification of domestication genes in crop wild relatives via genome editing—is an approach for harnessing the beneficial genetic diversity of crop wild relatives. A prerequisite for de novo domestication is phenotyping to identify genetic materials suitable for cultivation in the respective environment. Taxa from the wheat genepool (Triticum aestivum, Triticum durum, Triticum monococcum) are a staple food; these taxa comprise wild relatives of different ploidy levels. The diploid Triticum boeoticum and Triticum urartu and the tetraploid Triticum dicoccoides and Triticum araraticum harbor desirable traits such as high grain quality and abiotic and biotic stress tolerance. Hence, they are candidates for de novo domestication. Here, we grew 111 wild wheats and 38 landraces originating predominantly from the Fertile Crescent and six modern wheat cultivars in a field in Giessen, Germany, to evaluate their environmental adaptability to the central European climate and to identify potential candidates and target traits for de novo domestication. We demonstrate that several wild taxa are suitable for cultivation in the central European environment and that they have distinct characteristics that need to be modified during de novo domestication. The normalized difference vegetation index and the thermal time to heading and flowering indicated excellent adaptability of some wheat wild relatives to central European conditions. The values of yield parameters such as grain weight per plant, number of tillers, and thousand kernel weight were lower in the wild wheats than in the landraces. Therefore, these traits should be targeted for improvement during the de novo domestication of wild wheats.

A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.

Common bean (Phaseolus vulgaris) seeds are important legume crops and an important source of dietary proteins and carbohydrates. Therefore, it is important to develop strategies to improve salt tolerance in common beans. In this study, transcriptome and metabolome analyses were conducted on local common bean variety under salt stress at the sprout stage for a period of 0, 12, and 24 h. Results showed that phenylpropanoid pathways (including phenylpropanoid biosynthesis and phenylalanine metabolism) and flavonoid pathways (including flavonoid biosynthesis and flavone and flavonol biosynthesis) played an important role in controlling responses to salt stress as evidenced by analysis of differentially expression genes, common expression patterns, WCGNA, and differentially altered metabolites (DAMs) analyses. In addition, exploration of the activities of 4-coumarate-CoA ligase (4CL), caffeoyl-CoA O-methyltransferase (CCoAOMT), peroxidase (POD), chalcone isomerase (CHI), dihydroflavonol-4-reductase (DFR), and flavonol synthase (FLS) further showed that phenylpropanoid and flavonoid pathways participate in plant responses to salt stress. Moreover, the phenylpropanoid pathways and flavonoid pathways were found to be potential pathways regulating plant response to salt stress based on transcriptome and metabolome analysis. The activities of 4CL, CCoAOMT, POD, CHI, DFR, and FLS revealed that these pathways are crucial to the regulation of plant responses to salt stress. These findings provided theoretical basis for further improvement of salt tolerance in common bean.

Tree architectural attributes demonstrate a significant association with fruit yield. Yellowhorn is the future bioenergy tree in China; however, the species suffers from high reproductive energy and exceedingly low reproductive output. To optimize yellowhorn management and pinpoint priority trees featuring optimal architecture, we employed machine learning modeling to develop high fruit yielding predictive models using five yield indicators (dependent variables: FrW, SeW, ShW, FrW, and SeN) and five tree characteristics (independent variables: CA, TH, DGL, HLC, and MBN) of yellowhorn. Results showed that trees characterized by a substantial canopy area (>1.70 m²) and a large diameter at ground level (>3.71 cm) have been found to yield a higher fruit production. However, increased tree height does not invariably correlate with an elevated yield. Effective selection of high-yielding individuals can be accomplished by restricting tree height within the range of 192–232.4 cm. This approach emphasizes the importance of integrating considerations of tree architecture into forestry management practices. Such integration can bolster productivity, thereby contributing to both the sustainability and economic viability of yellowhorn forests.

Lodging is one of the main problems affecting the maize production. In this study, 220 maize inbred lines were used for determining the lodging resistance. Analysis methods such as the correlation analysis, the principal component analysis, the cluster analysis, the stepwise discriminate analysis, and the ridge regression analysis were deployed for data interpretation. The results of the correlation analysis showed that 17 characters were correlated with varying degrees. Strong and positive correlations between TID and FID (r = 0.910), TIL and FIL (r = 0.898), NSVB and NLVB (r = 0.775), ASVB and ALVB (r = 0.746), and LC and HC (r = 0.656) were observed. The first six principal components explained 79.13% of the phenotypic variation of the 17 characters, with the contribution rates being 20.77%, 18.12%, 14.09%, 11.17%, 8.66%, and 6.32%, respectively. Five categories were clustered in the 220 inbred lines. The result of stepwise discriminate analysis showed that 211 inbred lines were correctly discriminated and the identification rate was 95.91%, and 9 inbred lines were incorrectly discriminated and the identification rate was 4.09%, which meant that the result of cluster analysis was accurate and reliable. The stalk bending strength, ear height, cellulose content, number of small vascular bundles, and cross-sectional area were selected, and the regression model of lodging resistance of inbred lines was established by using the ridge regression method. Thirty of the 220 inbred lines, including R1656, 4003, and LD61, showed the highest lodging resistance. The results provide a reference for the selection of lodging-resistant germplasm resources in breeding for the lodging resistance hybrids.

Maize monoculture is one of the major restrictions limiting maize productivity in Western Ethiopia. Although the inclusion of legumes in cropping systems is an essential approach for the sustainable management of farming systems and for reducing the nitrogen (N) fertilizer requirement for maize production in the long term, the effects of soybean on the sustainability of maize productivity and soil fertility are unclear in Ethiopia. Continuous cropping of maize has led to extensive degradation of soil and a decrease in crop productivity in Western Ethiopia. Thus, the study was conducted to compare the long-term impact of soybean on the sustainability of the production system in soybean–maize rotation and to monitor soil fertility dynamics in soybean–maize rotational systems. Nine different soybean–maize rotation treatments were laid out in Randomized Complete Block Design (RCBD) with three replications. The study results showed that soybean–maize rotation gave a relatively steady yield compared to the maize mono-cropping system. Soybean–maize rotation improves the productivity of component crops in cropping systems. The highest maize grain yield was recorded from soybean–maize rotation with fertilizer application for the two components (RS + M+) and soybean–maize rotation without fertilizer application for the soybean component (RS-M+), respectively. Soybean grain yield was significantly correlated with OC (%), OM (%), and TN (%), whereas maize yield was adversely correlated with soil parameters, except soil pH. Overall, the knowledge we can contribute to the readers from this study is that soybean–maize rotation plays an important role in achieving sustainable agriculture by increasing soil fertility and achieving stable soybean and maize yields and soybean should be inoculated with Rhizobium strain year after year continuously. Thus, it can be concluded that soybean–maize rotation with fertilizer applications (RS + M+) for two components can be used in maize belt areas of Western Ethiopia.