Non-farm income and environmental efficiency of the farmers: Evidence from India

In the face of various agro-climatic shocks when agricultural income becomes highly volatile, farmers often undertake multiple jobholding and engage in non-farm activities for income smoothing. The earnings from these activities are often used to purchase productivity enhancing agricultural inputs. In this context, the impact of non-farm income on intensification of agricultural inputs and the consequent impact on over-all farm efficiency is well documented in the literature. However, with a rapid rise in usage of agricultural inputs with environmentally detrimental impact, very little is known about whether non-farm income has any impact on farmers' environmental efficiency-ability to reduce the amount of polluting inputs to the largest extent possible without reducing the amount of agricultural production. Our study fills the gap in the literature by analysing the impact of non-farm income on environmental efficiency of the farmers. We first estimate the environmental efficiency scores adopting the non-parametric data envelopment analysis (DEA) method and using a household level panel data from Village Dynamics of South Asia project on Indian states for a span of five years (2010-2014). We then estimate the impact of non-farm income on environmental efficiency using Instrumental Variable Tobit Model. Our results show that average environmental efficiency of the Indian farmers was 46 % during the study period indicating the fact that a reduction in polluting agricultural inputs by 54 % was possible without compromising the level of farm production. We also find that for 1 % increase in non-farm income, environmental efficiency of farmers rises by around 4 %. This reflects the environment friendly behaviour of farmers as a channel through which non-farm activities affect usage of environmentally linked inputs. These results provide vital policy insights in terms of how non-farm activities could be integrated with policies related to farming, in order to ensure sustainable agricultural practices.

CRISPR/Cas9-mediated mutagenesis of phytoene desaturase in pigeonpea and groundnut

The CRISPR/Cas9 technology, renowned for its ability to induce precise genetic alterations in various crop species, has encountered challenges in its application to grain legume crops such as pigeonpea and groundnut. Despite attempts at gene editing in groundnut, the low rates of transformation and editing have impeded its widespread adoption in producing genetically modified plants. This study seeks to establish an effective CRISPR/Cas9 system in pigeonpea and groundnut through Agrobacterium-mediated transformation, with a focus on targeting the phytoene desaturase (PDS) gene. The PDS gene is pivotal in carotenoid biosynthesis, and its disruption leads to albino phenotypes and dwarfism. Two constructs (one each for pigeonpea and groundnut) were developed for the PDS gene, and transformation was carried out using different explants (leaf petiolar tissue for pigeonpea and cotyledonary nodes for groundnut). By adjusting the composition of the growth media and refining Agrobacterium infection techniques, transformation efficiencies of 15.2% in pigeonpea and 20% in groundnut were achieved. Mutation in PDS resulted in albino phenotype, with editing efficiencies ranging from 4 to 6%. Sequence analysis uncovered a nucleotide deletion (A) in pigeonpea and an A insertion in groundnut, leading to a premature stop codon and, thereby, an albino phenotype. This research offers a significant foundation for the swift assessment and enhancement of CRISPR/Cas9-based genome editing technologies in legume crops.

Nitrate-responsive transcriptome analysis of rice RGA1 mutant reveals the role of G-protein alpha subunit in negative regulation of nitrogen-sensitivity and use efficiency

KEY MESSAGE

Nitrate-responsive transcriptomic, phenotypic and physiological analyses of rice RGA1 mutant revealed many novel RGA1-regulated genes/processes/traits related to nitrogen use efficiency, and provided robust genetic evidence of RGA1-regulation of NUE. Nitrogen (N) use efficiency (NUE) is important for sustainable agriculture. G-protein signalling was implicated in N-response/NUE in rice, but needed firm genetic characterization of the role of alpha subunit (RGA1). The knock-out mutant of RGA1 in japonica rice exhibited lesser nitrate-dose sensitivity than the wild type (WT), in yield and NUE. We, therefore, investigated its genomewide nitrate-response relative to WT. It revealed 3416 differentially expressed genes (DEGs), including 719 associated with development, grain yield and phenotypic traits for NUE. The upregulated DEGs were related to photosynthesis, chlorophyll, tetrapyrrole and porphyrin biosynthesis, while the downregulated DEGs belonged to cellular protein metabolism and transport, small GTPase signalling, cell redox homeostasis, etc. We validated 26 nitrate-responsive DEGs across functional categories by RT-qPCR. Physiological validation of nitrate-response in the mutant and the WT at 1.5 and 15 mM doses revealed higher chlorophyll and stomatal length but decreased stomatal density, conductance and transpiration. The consequent increase in photosynthesis and water use efficiency may have contributed to better yield and NUE in the mutant, whereas the WT was N-dose sensitive. The mutant was not as N-dose-responsive as the WT in shoot/root growth, productive tillers and heading date, but equally responsive as WT in total N and protein content. The RGA1 mutant was less impacted by higher N-dose or salt stress in terms of yield, protein content, photosynthetic performance, relative water content, water use efficiency and catalase activity. PPI network analyses revealed known NUE-related proteins as RGA1 interactors. Therefore, RGA1 negatively regulates N-dose sensitivity and NUE in rice.

An emerging role of heterotrimeric G-proteins in nodulation and nitrogen sensing

MAIN CONCLUSION

A comprehensive understanding of nitrogen signaling cascades involving heterotrimeric G-proteins and their putative receptors can assist in the production of nitrogen-efficient plants. Plants are immobile in nature, so they must endure abiotic stresses including nutrient stress. Plant development and agricultural productivity are frequently constrained by the restricted availability of nitrogen in the soil. Non-legume plants acquire nitrogen from the soil through root membrane-bound transporters. In depleted soil nitrogen conditions, legumes are naturally conditioned to fix atmospheric nitrogen with the aid of nodulation elicited by nitrogen-fixing bacteria. Moreover, apart from the symbiotic nitrogen fixation process, nitrogen uptake from the soil can also be a significant secondary source to satisfy the nitrogen requirements of legumes. Heterotrimeric G-proteins function as molecular switches to help plant cells relay diverse stimuli emanating from external stress conditions. They are comprised of Gα, Gβ and Gγ subunits, which cooperate with several downstream effectors to regulate multiple plant signaling events. In the present review, we concentrate on signaling mechanisms that regulate plant nitrogen nutrition. Our review highlights the potential of heterotrimeric G-proteins, together with their putative receptors, to assist the legume root nodule symbiosis (RNS) cascade, particularly during calcium spiking and nodulation. Additionally, the functions of heterotrimeric G-proteins in nitrogen acquisition by plant roots as well as in improving nitrogen use efficiency (NUE) have also been discussed. Future research oriented towards heterotrimeric G-proteins through genome editing tools can be a game changer in the enhancement of the nitrogen fixation process. This will foster the precise manipulation and production of plants to ensure global food security in an era of climate change by enhancing crop productivity and minimizing reliance on external inputs.

Weighted gene co-expression network analysis of nitrogen (N)-responsive genes and the putative role of G-quadruplexes in N use efficiency (NUE) in rice

Rice is an important target to improve crop nitrogen (N) use efficiency (NUE), and the identification and shortlisting of the candidate genes are still in progress. We analyzed data from 16 published N-responsive transcriptomes/microarrays to identify, eight datasets that contained the maximum number of 3020 common genes, referred to as N-responsive genes. These include different classes of transcription factors, transporters, miRNA targets, kinases and events of post-translational modifications. A Weighted gene co-expression network analysis (WGCNA) with all the 3020 N-responsive genes revealed 15 co-expression modules and their annotated biological roles. Protein-protein interaction network analysis of the main module revealed the hub genes and their functional annotation revealed their involvement in the ubiquitin process. Further, the occurrences of G-quadruplex sequences were examined, which are known to play important roles in epigenetic regulation but are hitherto unknown in N-response/NUE. Out of the 3020 N-responsive genes studied, 2298 contained G-quadruplex sequences. We compared these N-responsive genes containing G-quadruplex sequences with the 3601 genes we previously identified as NUE-related (for being both N-responsive and yield-associated). This analysis revealed 389 (17%) NUE-related genes containing G-quadruplex sequences. These genes may be involved in the epigenetic regulation of NUE, while the rest of the 83% (1811) genes may regulate NUE through genetic mechanisms and/or other epigenetic means besides G-quadruplexes. A few potentially important genes/processes identified as associated with NUE were experimentally validated in a pair of rice genotypes contrasting for NUE. The results from the WGCNA and G4 sequence analysis of N-responsive genes helped identify and shortlist six genes as candidates to improve NUE. Further, the hitherto unavailable segregation of genetic and epigenetic gene targets could aid in informed interventions through genetic and epigenetic means of crop improvement.

India's Riverine Nitrogen Runoff Strongly Impacted by Monsoon Variability

Agricultural intensification in India has increased nitrogen pollution, leading to water quality impairments. The fate of reactive nitrogen applied to the land is largely unknown, however. Long-term records of riverine nitrogen fluxes are nonexistent and drivers of variability remain unexamined, limiting the development of nitrogen management strategies. Here, we leverage dissolved inorganic nitrogen (DIN) and discharge data to characterize the seasonal, annual, and regional variability of DIN fluxes and their drivers for seven major river basins from 1981 to 2014. We find large seasonal and interannual variability in nitrogen runoff, with 68% to 94% of DIN fluxes occurring in June through October and with the coefficient of variation across years ranging from 44% to 93% for individual basins. This variability is primarily explained by variability in precipitation, with year- and basin-specific annual precipitation explaining 52% of the combined regional and interannual variability. We find little correlation with rising fertilizer application rates in five of the seven basins, implying that agricultural intensification has thus far primarily impacted groundwater and atmospheric emissions rather than riverine runoff. These findings suggest that riverine nitrogen runoff in India is highly sensitive to projected future increases in precipitation and intensification of the seasonal monsoon, while the impact of projected continued land use intensification is highly uncertain.

Nitrate-responsive transcriptome analysis reveals additional genes/processes and associated traits viz. height, tillering, heading date, stomatal density and yield in japonica rice

Our transcriptomic analysis expanded the repertoire of nitrate-responsive genes/processes in rice and revealed their phenotypic association with root/shoot, stomata, tiller, panicle/flowering and yield, with agronomic implications for nitrogen use efficiency. Nitrogen use efficiency (NUE) is a multigenic quantitative trait, involving many N-responsive genes/processes that are yet to be fully characterized. Microarray analysis of early nitrate response in excised leaves of japonica rice revealed 6688 differentially expressed genes (DEGs), including 2640 hitherto unreported across multiple functional categories. They include transporters, enzymes involved in primary/secondary metabolism, transcription factors (TFs), EF-hand containing calcium binding proteins, hormone metabolism/signaling and methytransferases. Some DEGs belonged to hitherto unreported processes viz. alcohol, lipid and trehalose metabolism, mitochondrial membrane organization, protein targeting and stomatal opening. 1158 DEGs were associated with growth physiology and grain yield or phenotypic traits for NUE. We identified seven DEGs for shoot apical meristem, 66 for leaf/culm/root, 31 for tiller, 70 for heading date/inflorescence/spikelet/panicle, 144 for seed and 78 for yield. RT-qPCR validated nitrate regulation of 31 DEGs belonging to various important functional categories/traits. Physiological validation of N-dose responsive changes in plant development revealed that relative to 1.5 mM, 15 mM nitrate significantly increased stomatal density, stomatal conductance and transpiration rate. Further, root/shoot growth, number of tillers and grain yield declined and panicle emergence/heading date delayed, despite increased photosynthetic rate. We report the binding sites of diverse classes of TFs such as WRKY, MYB, HMG etc., in the 1 kb up-stream regions of 6676 nitrate-responsive DEGs indicating their role in regulating nitrate response/NUE. Together, these findings expand the repertoire of genes and processes involved in genomewide nitrate response in rice and reveal their physiological, phenotypic and agronomic implications for NUE.

Sustainable intensification of maize and wheat cropping system through pulse intercropping

The intercropping of legumes with cereals help to achieve sustainable intensification by their mutual complementarity at efficiently using radiation, nutrients, etc. Several studies indicated such beneficial effects on the other component crop however, little research has been conducted to quantify their effects on the subsequent crop in a cropping system. In this study, the effect of the legume intercropping on the entire cropping system, particularly the maize + legume-wheat system was studied. Four legumes intercropped to maize followed by wheat crop were studied for intensification measures such as wheat equivalent yield (WEY), land equivalent ratio (LER), sustainable value index (SVI), and economic returns. N saving effect of legumes on the subsequent wheat crop was quantified with two N levels. Maize + cowpea-wheat combination was the most productive and economic intercrop combination (LER = 1.71, SVI = 0.96) with an increase in net economic return (43.63%) with a B:C ratio of 1.94. An additional 25% N (37.5 kg ha^-1) was saved in the wheat crop when the legume intercropping was undertaken with maize. The results suggest that intercropping is the key to diversification and reduces the risk of crop failures by enhancing land-use efficiency, soil fertility, and economic returns under weather vagaries. This will be beneficial to small and marginal farmers of many countries.

Towards Precision Fertilization: Multi-Strategy Grey Wolf Optimizer Based Model Evaluation and Yield Estimation

Precision fertilization is a major constraint in consistently balancing the contradiction between land resources, ecological environment, and population increase. Even more, it is a popular technology used to maintain sustainable development. Nitrogen (N), phosphorus (P), and potassium (K) are the main sources of nutrient income on farmland. The traditional fertilizer effect function cannot meet the conditional agrochemical theory’s conditional extremes because the soil is influenced by various factors and statistical errors in harvest and yield. In order to find more accurate scientific ratios, it has been proposed a multi-strategy-based grey wolf optimization algorithm (SLEGWO) to solve the fertilizer effect function in this paper, using the “3414” experimental field design scheme, taking the experimental field in Nongan County, Jilin Province as the experimental site to obtain experimental data, and using the residuals of the ternary fertilizer effect function of Nitrogen, phosphorus, and potassium as the target function. The experimental results showed that the SLEGWO algorithm could improve the fitting degree of the fertilizer effect equation and then reasonably predict the accurate fertilizer application ratio and improve the yield. It is a more accurate precision fertilization modeling method. It provides a new means to solve the problem of precision fertilizer and soil testing and fertilization.