Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R2, root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R2 ranging between 367 and 470 kg ha−1, 276–345 kg ha−1, and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha−1 for nitrogen application rate to 372 kg ha−1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha−1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data

A Compendium of Key Climate Smart Agriculture Practices in Intensive Cereal Based Systems of South Asia

CSA initially proposed by FAO in 2010 at “The Hague Conference on Agriculture, Food Security and Climate Change (CC)”, to address the need for a strategy to manage agriculture and food systems, under climate change. The CSA by its original proponents describes the three objectives; i) sustainably increasing agricultural productivity to support equitable increases in incomes, food security and development; ii) adapting and building resilience to climate change from the farm to national levels; and iii) developing opportunities to reduce GHG emissions from agriculture compared with past trends. Since then, these three objectives (in short food security, adaptation and mitigation) are designated as the three “pillars” (or criteria) of CSA within the agricultural science and development communities. Climate Smart (Sustainable Management of Agricultural Resources and Techniques) Agriculture is an approach of crop production, which deals with the management of available agricultural resources with latest management practices and farm machinery, under a particular set of edaphic and environmental conditions. It works to enhance the achievement of national food security and Sustainable Development Goals (SDGs). CSA is location specific and tailored to fit the agro-ecological and socio-economic conditions of a location. CSA may be defined as “agriculture that sustainably increases productivity, resilience (adaptation), reduces/removes greenhouse gases (mitigation), and enhances achievement of national food security and development goals.” Therefore, if CSA implemented at right time with required resources, techniques and knowledge in a particular typological domain, will lead towards food security while improving adaptive capacity and mitigating potential for sustainable agriculture production

Effect of FYM and Inorganic Fertilizers on Nutrient Content, Uptake and Quality Traits of Wheat (Triticum aestivum L.) under Indo-Gangetic Plain of Uttar Pradesh

A field trial was conducted on sandy loam soil having low status of organic carbon and accessible nitrogen, medium in accessible phosphorous and high in accessible potassium at pot house of department of Soil Science and Agricultural Chemistry of C.S.A.U.A&T, Kanpur (campus) under Indo-Gangetic Plain zone of Uttar Pradesh, amid Rabi season of 2018-19. The experiment comprised of 5 treatment combinations in randomized block design with four replications consisted of T1: [Control], T2: [100% RDF], T3: [75% RDF + FYM at 6 t ha-1], T4: [50% RDF + FYM at 12 t ha-1], T5: [25% RDF + FYM at 18 t ha-1]. Wheat variety PBW-343 was grown with the recommended agronomic practices. On the premise of the comes about exuded from the present investigation, it might be concluded that application of 25% RDF + FYM at18 t ha-1 significantly recorded maximum nutrient content viz. N, P and K content in grain is 1.97%, 0.25% and 0.36% respectively and N,P, and K content in straw is 0.32%, 0.064% and 1.76% respectively. Maximum nutrient uptake viz. N, P and K uptake in grain is 86.58 %, 10.77% and 5.85% respectively and N, P, and K uptake in straw is 22.98%, 4.16% and 1.76 % respectively. Among the quality traits maximum protein content (11.78 %) was also associated with application of 25% RDF + FYM at 18 t ha-1. The present investigation clearly points out the significance of balanced use of nutrients including FYM in wheat for improving the nutrient content and uptake indices and quality of wheat crop

Impact of Soil Salinity on Citrus: A Review

Citrus fruits are one of the most important fruit crops in the world. However, these are vulnerable to a variety of environmental stresses, including drought, over watering (water logging), extreme temperatures (cold, frost, and heat), salinity, and mineral toxicity. Above these factors, salinity is the one of the most significant. Because of its hyperosmotic and hyperionic effects on the soil rhizosphere, salinity is an abiotic factor that has detrimental effect of reducing plant development and yield. Citrus is a salt-susceptible crop as compared to other fruit crops, because citrus development and yield are dramatically reduced under salt stress conditions. There are a variety of approaches that can be used to mitigate the harmful effects of salinity, including alternative irrigation and the selection of salt-resistant root stocks. This review will therefore, concentrate on the influence of soil salinity on citrus production and feasible mitigation techniques to reduce production losses

Compton profile of palladium

In this paper we present the results of a Compton-profile study on polycrystalline palladium. The measurements have been made by scattering 59.54-keV photons. Theoretical Compton profiles have been calculated with use of the renormalized-free-atom (RFA) model and the augmented-plane-wave method. Best agreement between the measured and calculated values was found for the 4d9.75s0.3 configuration within the RFA model

Precise irrigation water and nitrogen management improve water and nitrogen use efficiencies under conservation agriculture in the maize-wheat systems

Abstract A 3-year field experiment was setup to address the threat of underground water depletion and sustainability of agrifood systems. Subsurface drip irrigation (SDI) system combined with nitrogen management under conservation agriculture-based (CA) maize-wheat system (MWS) effects on crop yields, irrigation water productivity (WPi), nitrogen use efficiency (NUE) and profitability. Grain yields of maize, wheat, and MWS in the SDI with 100% recommended N were significantly higher by 15.8%, 5.2% and 11.2%, respectively, than conventional furrow/flood irrigation (CT-FI) system. System irrigation water savings (~ 55%) and the mean WPi were higher in maize, wheat, and MWS under the SDI than CT-FI system. There was saving of 25% of fertilizer N in maize and MWS whereas no saving of N was observed in wheat. Net returns from MWS were significantly higher (USD 265) under SDI with 100% N (with no subsidy) than CT-FI system despite with higher cost of production. The net returns were increased by 47% when considering a subsidy of 80% on laying SDI system. Our results showed a great potential of complementing CA with SDI and N management to maximize productivity, NUE, and WPi, which may be economically beneficial and environmentally sound in MWS in Trans-IGP of South Asia

Effect of Integrated Nutrient Management on Growth Parameters, Yield Components and Yield of Wheat (Triticum aestivum L.) under Central Plain Zone of Uttar Pradesh

Field experiments were conducted to studies effect of integrated nutrient management on growth parameters, yield components and yield of wheat during rabi season of 2020-21 and 2021-22 at students instructional farm, Chandra Shekhar Azad University of Agriculture & Technology, Kanpur. The experiment consist of 10 treatments combinations in randomized block design with three replications consisted of different combination of inorganic fertilizer, organic manure and biofertilizer. Wheat variety HD-2967 was grown with the recommended agronomic practices. On the basis of results emanated from investigation it can be concluded that among the growth parameters maximum plant height at maturity was 109.25 cm and 110.12, maximum number of effective tillers is 352.67 and 355.72 and maximum spike length is 13.55 cm and 13.79 cm are associated with the treatment T10 [100%NPK + FYM + S30+ Zn5 +Azotobacter + PSB] during the both years of experimentation. Similarly, among the yield components and productivity parameters maximum values in relation to number of spikelet ear-1, grain ear-1, 1000 grain wt. (gm), grain yield (q ha-1) and straw yield (q ha-1) were found in the treatment T10 [100%NPK+FYM+S30+Zn5+Azotobacter+ PSB]

Artificial night light alters ecosystem services provided by biotic components

The global catastrophe of natural biodiversity and ecosystem services are expedited with the growing human population. Repercussions of artificial light at night ALAN are much wider, as it varies from unicellular to higher organism. Subsequently, hastened pollution and over exploitation of natural resources accelerate the expeditious transformation of climatic phenomenon and further cause global biodiversity losses. Moreover, it has a crucial role in global biodiversity and ecosystem services losses via influencing the ecosystem biodiversity by modulating abundance, number and aggregation at every levels as from individual to biome levels. Along with these affects, it disturbs the population, genetics and landscape structures by interfering inter- and intra-species interactions and landscape formation processes. Furthermore, alterations in normal light/dark (diurnal) signalling disrupt the stable physiological, biochemical, and molecular processes and modulate the regulating, cultural and provisioning ecosystem services and ultimately disorganize the stable ecosystem structure and functions. Moreover, ALAN reshapes the abiotic component of the ecosystem, and as a key component of global warming via producing greenhouse gases via emitting light. By taking together the above facts, this review highlights the impact of ALAN on the ecosystem and its living and non-living components, emphasizing to the terrestrial and aquatic ecosystem. Further, we summarize the means of minimizing strategies of ALAN in the environment, which are very crucial to reduce the further spread of night light contamination in the environment and can be useful to minimize the drastic impacts on the ecosystem