Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

Zinc and nitrogen mediate the regulation of growth, leading to the upregulation of antioxidant aptitude, physio-biochemical traits, and yield in wheat plants

An ample amount of water and soil nutrients is required for economic wheat production to meet the current food demands. Nitrogen (N) and zinc (Zn) fertigation in soils can produce a substantial wheat yield for a rapidly increasing population and bring a limelight to researchers. The present study was designed to ascertain N and Zn's synergistic role in wheat growth, yield, and physio-biochemical traits. A pot experiment was laid out under a complete randomized design with four N levels (N1-0, N2-60, N3- 120, and N4-180 kg ha-1), Zn (T1-0, T2-5, T3-10, and T4-15 kg ha-1) with four replications. After the emergence of the plants, N and Zn fertigation was applied in the soil. The growth traits were considerably increased by combined applications as compared to the sole applications of the N and Zn. The photosynthetic pigments were found maximum due to combined applications of N and Zn, which were positively associated with biomass, growth, yield, and wheat grain quality. The combined application also substantially enhances the antioxidant enzyme activities to scavenge the ROS as H2O2 and reduce lipid peroxidation to protect the permeability of the biologic membranes. The combined higher applications of N and Zn were more responsive to ionic balance in a shoot by maintaining the Na+ for osmotic adjustments, accumulating more Ca2+ for cellular signaling; but, combined applications resulted in K+ reduction. Our present results suggest that appropriate sole or combined applications of N and Zn improve wheat's growth, yield, and antioxidant mechanisms. Previous studies lack sufficient information on N and Zn combined fertigation. We intend to investigate both the sole and combined roles of N and Zn to exploit their potential synergistic effects on wheat.

Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize (Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann-Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson's correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO2). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO2 (5.1 units/decade), and a downward trend in DTR ( - 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Variability in morpho-biochemical, photosynthetic pigmentation, enzymatic and quality attributes of potato for salinity stress tolerance

Salt stress has emerged as a growing global concern, exerting a significant impact on agricultural productivity. The challenges of salt stress on potatoes are crucial for ensuring food security and sustainable agriculture. To address this issue a pot trial was executed to evaluate the impacts of NaCl in the soil on the growth, photosynthetic pigments, and quality attributes of potato, plants were grown in soil spiked with various concentrations of NaCl (0, 1, 3, 5, 7 g kg-1 of soil). Results revealed that salt stress have negative impacts on the growth, biomass, photosynthesis and quality attributes of potato. Lower level of salt stress 1 g kg-1 of soil improved the fresh and dry biomass of leaves (78.70 and 47.74%) and tubers (86.04 and 88.92%) as compared to control, respectively. Higher levels of salt stress (7 g kg-1) increased lipid peroxidation in leaves and improved the enzymatic antioxidants. It was observed that enzyme activities i.e., SOD (134.97%), POD (101.02%), and CAT (28.87%) increased in leaves and are inversely related to the NaCl concentration. The combination of reduction in chlorophyll contents and soluble sugars resulted in lower levels of quality attributes i.e., amylose (68.90%) and amylopectin (16.70%) of potato. Linear relationship in growth, biomass and physiological attributes showed the strong association with increased salt stress. Furthermore, the PCA-heatmap synergy offers identifying clusters of co-regulated attributes, which pinpoint the physiological responses that exhibit the strongest correlation with increasing salt stress levels. Findings indicate that potato can be grown successfully with (1 g kg-1 of NaCl in soil) without negative impacts on plant quality. Furthermore, this study contributes valuable insights into the complexities of salt stress on potato plants and provides a foundation for developing strategies to enhance their resilience in salt-affected environments.

Effect of dietary seaweed (Ascophyllum nodosum) supplementation on milk mineral concentrations, transfer efficiency, and hematological parameters in lactating Holstein cows

This study investigated the effect of feeding seaweed (Ascophyllum nodosum) to dairy cows on milk mineral concentrations, feed-to-milk mineral transfer efficiencies, and hematological parameters. Lactating Holstein cows (n = 46) were allocated to 1 of 2 diets (n = 23 each): (1) control (CON; without seaweed) and (2) seaweed (SWD; replacing 330 g/d of dried corn meal in CON with 330 g/d dried A. nodosum). All cows were fed the CON diet for 4 wk before the experiment (adaptation period), and animals were then fed the experimental diets for 9 wk. Samples included sequential 3-wk composite feed samples, a composite milk sample on the last day of each week, and a blood sample at the end of the study. Data were statistically analyzed using a linear mixed effects model with diet, week, and their interaction as fixed factors; cow (nested within diet) as a random factor; and data collected on the last day of the adaptation period as covariates. Feeding SWD increased milk concentrations of Mg (+6.6 mg/kg), P (+56 mg/kg), and I (+1,720 μg/kg). It also reduced transfer efficiency of Ca, Mg, P, K, Mn, and Zn, and increased transfer efficiency of Mo. Feeding SWD marginally reduced milk protein concentrations, whereas there was no effect of SWD feeding on cows' hematological parameters. Feeding A. nodosum increased milk I concentrations, which can be beneficial when feed I concentration is limited or in demographics or populations with increased risk of I deficiency (e.g., female adolescents, pregnant women, nursing mothers). However, care should also be taken when feeding SWD to dairy cows because, in the present study, milk I concentrations were particularly high and could result in I intakes that pose a health risk for children consuming milk.

Conservation and Global Distribution of Onion (Allium cepa L.) Germplasm for Agricultural Sustainability

Onion (Allium cepa L.) is recognized globally as a crucial vegetable crop, prized not only for its culinary applications but also for its numerous health-promoting properties. With climate change relentlessly exerting mounting challenges to agriculture, the preservation and deployment of onion germplasm has become critical to ensuring sustainable agriculture and safeguarding food security. Global onion germplasm collections function as repositories of genetic diversity, holding within them an extensive array of valuable traits or genes. These can be harnessed to develop varieties resilient to climate adversities. Therefore, detailed information concerning onion germplasm collections from various geographical regions can bolster their utility. Furthermore, an amplified understanding of the importance of fostering international and inter-institutional collaborations becomes essential. Sharing and making use of onion genetic resources can provide viable solutions to the looming agricultural challenges of the future. In this review, we have discussed the preservation and worldwide distribution of onion germplasm, along with its implications for agricultural sustainability. We have also underscored the importance of international and interinstitutional collaboration in onion germplasm collecting and conservation for agricultural sustainability.

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world's inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R 2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R 2 for Aus; 0.92 MSE, 90% R 2 for Aman; 0.246 MSE, 99% R 2 for Boro; 0.062 MSE, 99% R 2 for wheat; and 0.016 MSE, 99% R 2 for potato production prediction. The Diebold-Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

Developing an analytical framework for estimating food security indicators in the United Arab Emirates: A review

Rapid population growth, climate change, limited natural resources, and the COVID-19 pandemic contribute to increased global hunger, necessitating intensive efforts to ensure food security and nutrition (FSN). Previous FSN approaches covered some dimensions, but not all, resulting in significant gaps in food security indicators. The Gulf Cooperation Council (GCC) and the Middle East and North Africa (MENA) regions have received less attention in food security studies, thus far necessitating considerable effort to develop an appropriate analytical framework. This study reviewed articles and international reports of FSN indicators, drivers and policies, methods, and models and extracted the challenges and gaps from the global and UAE contexts. The UAE and the world have gaps in FSN drivers, indicators, and methods, necessitating potential solutions to meet future challenges such as rapid population growth, pandemics, and limited natural resources. As a result, we created a newly developed analytical framework that addresses the shortcomings of previous approaches such as sustainable food systems developed by FAO and the Global Food Security Index (GFSI) and covers all aspects of food security. Gaps in knowledge in FSN drivers and policies, indicators, big data, methods, and models were considered in the developed framework, which has specific advantages. The novel developed framework addresses all food security dimensions (access, availability, stability, and utilization), ensuring poverty reduction, food security, and nutrition security while outperforming previous approaches (i.e., FAO and GFSI). The developed framework could be used successfully not only in the UAE and MENA, but also, globally, helping to solve food insecurity and malnutrition for future generations. The scientific community and policymakers should disseminate such solutions to address global food insecurity and ensure nutrition for future generations in the face of rapid population growth, limited natural resources, climate change, and spreading pandemics.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03032-3.

The status and natural impact of floriculture production in Ethiopia: a systematic review

Ethiopia's floriculture industry is exceedingly emerging, and, currently, it ranks as the sixth largest exporter of roses worldwide and second largest in Africa. Currently, many flowers, such as rose, gypsophila, carnations, and chrysanthemum, are growing. However, floriculture farms are contributing a high level of health risks and environmental problems in Ethiopia. Thus, the purpose of this paper is to scrutinize the status and impacts of floriculture industries in Ethiopia. The floriculture division is an emerging sector in Ethiopia, and the number of farms, income, job opportunities, and diversity of flowers are increasing. However, the health risks and environmental fates of the sector are also increasing. Ethiopian floriculture farms lack waste disposal technologies and workers' protective equipment and safety, and the chemicals, plastics, and corrugated irons used in the farms are carelessly disposed everywhere. Pesticides, plastics, and fertilizers are also freely discharged into water bodies and terrestrial land, which is causing the development of health risks; aquatic life hazards; and soil, water, and air pollution. However, Ethiopia has no strong and functional system or structure to control the impacts of floriculture farms. The government and the farm owners are not thoughtful about the environmental issues, health concerns, and socioeconomic impacts of the wastes. The government lacks regular control and assessment of farms, and the farms are engaging for their profit. Nevertheless, developed countries are currently using both natural and modern technologies to manage floricultural wastes. Ethiopia should therefore suggest manageable possible approaches and sound management strategies based on the findings of the analyses.

Heat Shock Proteins and Antioxidant Genes Involved in Heat Combined with Drought Stress Responses in Perennial Rye Grass

The frequent occurrence of heat and drought stress can severely reduce agricultural production of field crops. In comparison to a single stress, the combination of both heat (H) and drought (D) further reduce plant growth, survival and yield. This study aimed to explore the transcriptional responses of heat shock protein (HSP) and antioxidant genes under H combined D stress in perennial rye grass (PRG). The results demonstrated that oxidative stress indicators (hydrogen peroxide, lipid peroxidation) significantly increased, particularly in the case of combined H and D treatment, suggesting that oxidative stress-induced damage occurred in plants under the combined stresses. Transcriptional responses of heat shock protein 70 (HSP70), heat shock protein 90-6 (HSP90-6), and the mitochondrial small heat shock protein HSP26.2 (HSP26.2) occurred rapidly, and showed high level of expression particularly under H and D stress. Antioxidant genes including ascorbate peroxidase (APX), glutathione reductase (GR), monodehydroascorbate reductase (MDHAR), dehydroascorbate reductase (DHAR), catalase (CAT), copper–zinc superoxide dismutase (Cu/ZnSOD), peroxidase (POD), ferredoxin–thioredoxin (FTR), thioredoxin (Trx), 2-cysteine peroxiredoxin (2-Cys Prx) showed response to combined H and D, followed by either D or H stress alone in rye grass. An interactome map revealed the close partnership of these heat shock protein genes and antioxidant genes, respectively. These candidate genes were predominantly linked to stress responses and antioxidant defense in plants. These findings may advance our understanding about the HSP and the antioxidant genes underlying combined abiotic stress response and tolerance in perennial rye grass.

Phenology forcing model to estimate phenology shifting ability of extreme environmental events

The current study considered the climate extreme index (CEI) values originated from extreme environmental events (EEEs) by following the National Oceanic and Atmospheric Administration (NOAA) guidelines. The EEEs were fractionated into six sub-categories (i.e., high temperature, low temperature, high precipitation, low precipitation, drought, and wind), and the combined impact of CEIs was utilized to develop an algorithm for the estimation of the phenology sensitivity index (P _Si ). Finally, the CEIs, and the P _Si were undergone the development of the phenology forcing (P^F ) model. The developed model showed a high sensitivity at the CEI value of as low as ≥1.0. Furthermore, the uncertainty index varied between 0.03 and 0.07, making a parabolic curvature at increasing CEIs (1.0-15.0). The current study precisely estimates the tendency of EEEs for phenology change. It will assist in policy-making and planning crop cultivation plans for achieving sustainable development goal 2 (SDG2) of the Food and Agriculture Organization (FAO).