International Journal of Plant Production
Volume:15 Issue: 2, Jun 2021

Global climate change escalates the rise of atmospheric CO2 concentration and temperature, which impact crop production in agricultural ecosystems. As the second important macronutrient, phosphorus (P) fundamentally mediates the crop adaptability to climate change. An overview on previous work on crop P acquisition and soil P dynamics in responses to elevated CO2 and temperature would be critical for further advancing our knowledge on P cycling under climate change and its management to maintain agroecosystem sustainability. This review focuses on the effects of elevated CO2 and temperature on root morphology, root exudation, and associated biochemical properties in the rhizosphere in relevant to crop P acquisition and soil P availability. Studies indicate that elevated CO2 and temperature could increase P uptake of crops, such as rice and soybean when crops are grown within the range of optimal growth temperature. Elevated CO2 and temperature not only alter root exudates and changes the activity of soil enzymes and microbes the in rhizosphere environment, but also directly influence soil chemical and biochemical processes and thus the bioavailability of P. It is worth to focus on P-solubilizing microbial community composition, and microbial function on soil P mobilization in the rhizosphere of crops grown under climate change.

To examine the rain-fed Aman rice yield fluctuation due to climatic anomalies overtimes in Bangladesh, we used climate-induced yield index (CIYI), ensemble empirical mode decomposition (EEMD), step-wise multiple regression, isotopic signature, wavelet transform coherence (WTC) and random forest (RF) model. In this work, daily multiple source climatic data which were collected between 1980 and 2017, from 18 weather stations and five atmospheric circulation indices were used for this purpose. The key findings were as follows; by employing principal component analysis (PCA), six temporal variability modes were identified as six corresponding sub-regions with various Aman rice CIYI fluctuations. The Aman rice CIYI in different sub-regions represented a noteworthy 3–4-year quasi-oscillation using the EEMD. The key climate variables (KCVs) including the potential evapotranspiration and cloud cover in September, the minimum temperature in August, and precipitation in July, August, and October were the best rice yield prediction signals in these sub-regions. The results suggest that Aman rice yield could likely decline by 33.59%, and 3.37% in the southwestern and southeastern regions, respectively, if KCV increased by 1 °C or 1%. The RF model suggests that the Indian Ocean Dipole (IOD) significantly influenced the rice yield. Isotopic signatures were employed to confirm the fluctuation and anti-amount effect during the Aman rice-growing period in Bangladesh. The results obtained in this study could be used as a guideline for sustainable mitigation and adaptation measures in managing agro-meteorological hazards in Bangladesh.

Narrowing the yield gaps is one of the major concerns in developing countries. Closing yield gap to obtain attainable yield is a viable option for providing information regarding the reason of yield loss. Hence, accurate estimation of the yield gap has many practical applications for enhancing production of crops. This research was conducted for assessing the yield gap of rice-grown fields using boundary-line analysis (BLA) and comparative performance analysis (CPA) methods. Collection of 100 rice-grown fields data were done in Sari region, Mazandaran province, one of the major rice production areas in northern Iran from 2015 to 2016. All paddy field management operations from preparation of nursery to harvest of yield has been recorded for local rice varieties. The CPA model calculate the potential yield and factors causing yield gap. In contrast, BLA model were fitted to the edge of data cloud of rice yield versus field managing variables from monitoring. Analysis of data in 100 monitored paddy fields demonstrated that rice yield varied from 3100 to 5430 kg ha−1. Prediction of potential yield for CPA and BLA methods were 5703 and 5369 kg ha−1, respectively. The yield gaps calculated by CPA and BLA methods in 1212 and 881 kg ha−1, respectively. In the CPA, the share of yield gap for variables entered in the model were 5% for cover crop of canola, 18% for legumes before rice cultivation, 4% for seed disinfection, 10% for seeding date in nursery, 11% for seedling age, 11% for seedling growth stage for transplanting, 5% for mechanized transplanting, 4% for fertilizer top-dressing, 27% for number of top-dressing and 6% for foliar application of nutrients. In the BLA, an average attainable yield, based on the optimum level of the 12 studied variables, was 5369 kg ha−1 with an 881 kg ha−1 yield gap. Regarding the fact that calculated yield in CPA and BLA, it has been stated that this potential yield is attainable. CPA and BLA are cheap and simple tools that, without the need for expensive experimentation, is able to detect yield gap and its causes in a district. Therefore, these methods can be used effectively in developing countries where the highest yield gaps exist.

Soil amendments such as biochar and biofertilizers may improve chickpea productivity but there is limited information on whether this response could be through an increase in soil pH and nodulation. We aimed to determine whether the previously observed positive effects of biochar and rhizobium inoculation on soil pH and chickpea nodulation would result in similar improvements in the proportion of radiation intercepted by the crop canopy, biomass accumulation, and grain yield of three desi chickpea genotypes. Field experiments were carried out in clay and loamy sand soils in two successive years. Biochar application and rhizobium inoculation increased biomass accumulation, chlorophyll content, the proportion of intercepted radiation, and decreased chlorophyll a/b ratios which suggests that biochar and rhizobium inoculation increased biomass accumulation by increasing antenna size and canopy cover. Although rhizobium inoculation increased grain yield of all genotypes, the increase was greater in the best performing genotype, Acc#6. Biomass was highest at 10 t ha−1 and 20 t ha−1 biochar in the clay and loamy sand soil, respectively, suggesting that the lighter soils require higher biochar rates compared to the heavier soils for optimal biomass accumulation.

Sensitivity analysis is helpful for improving the efficiency and accuracy of the calibration of crop growth models. However, parameter sensitivity is still not well understood when combined with different meteorological and production conditions, especially adverse conditions such as water stress. This study simulated the production of winter wheat in four ecological areas in Henan Province, China. The Extend Fourier Amplitude Sensitivity Test algorithm (EFAST) was used for analyzing the sensitivity of 43 crop parameters of the WOrld FOod STudies (WOFOST) model to yield, aboveground biomass, and leaf area index (LAI) with or without water-limited conditions. The results demonstrated that yield and biomass were the objective outputs, and the main limiting factors for the model results were assimilation and dry matter conversion efficiency. Under water-limited conditions, the parameter sensitivity of related extinction coefficient, early wheat leaf area, and root growth increased with increased water stress. With the process variable LAI as the target output, the parameter sensitivity varied at different growth stages, whereas the parameter sensitivity was almost the same under different agro-meteorological conditions. Under water-limited conditions, the parameter sensitivity of wheat early extinction coefficient, maximum root depth, and death rate of the leaves also increased with increased water stress. Therefore, water stress is a key factor affecting parameter sensitivity under different agro-meteorological conditions.

Nitrogen (N) losses from the N sources such as manures, fertilizers etc. applied to crops are considered as the largest non-point source of nitrogen-nitrate pollution in surface and groundwater bodies. The extent of water bodies polluted with N is worsening day by day, worldwide, with its severe impact on the quality of drinking water. This necessitates the development of crop specific N management practices to reduce N losses from crop systems. Improvements in agronomic and recovery efficiency of nitrogen in crops are regarded as promising techniques to reduce N losses. With the hypothesis that precise N supply in maize employing LCC or CCM under a critical threshold value will augment the yield performances and Nitrogen Use Efficiency (AEN and REN) of maize, while reducing N losses, a two year study was conducted at Anand, India. Fifty percent reduction in basal N application and subsequent N applications based on LCC critical value 5 resulted in 12.30 and 12.25% increment in maize grain yield over recommended practices during the year 2015 and 2016, respectively. Significant improvement for total biological yield, grain protein accumulation in maize and total N uptake by crop was observed in the direction of N application using LCC threshold point 5, CCM threshold point 40 and recommended practice. Applying nitrogen at whatever the times LCC critical point drops ≤ 5 also recorded 4.09 and 4.17 kg gain in grain produced (over recommended practice) kg−1 of N supplied (AEN), 0.16 and 0.17 kg gain in N uptake (over recommended practice) kg−1 of N supplied (REN) during 2015 and 2016, respectively and a total reduction of 51.14 kg N loss ha−1 (over recommended practice) for the entire study period. The study reveal that compared to blanket application, N fertilizers can be more efficiently managed with LCC threshold value 5 or CCM threshold value 40 for guiding N application with higher yield, NUE and reduced N losses in maize crop.

Due to environmental diversity, genotype performance for yield and stability is essential for crop improvement. The GGE biplot, and 11 parametric and non-parametric stability models were employed to evaluate 23 wheat (Triticum aestivum L.) genotypes, tested in randomized complete block trials across two contrasting fields (sandy and loamy) and four seasons. The sandy field yielded half compared to the loamy field, reflecting relatively low- and high-input environments, respectively. Analysis of variance showed significant differences between genotypes for grain yield and crossover genotype ranking across environments; the loamy field was more representative of an overall genotype performance. The stability models resulted in diverse genotype classification and were distinguished into two separate groups. The first group comprised measures that consider both G and GE focusing on the agronomic aspect of stability and high-yielding ability. The second group included tools that consider only GE focusing on the static aspect of stability and characterized most of the high-performing genotypes as undesirable. The GGE biplot highlighted genotypes that were characterized as either desirable or undesirable following most models in both groups. Therefore, the GGE biplot presented an effective statistical tool for assessing wheat genotypes in terms of general and specific adaptation without overlooking yielding ability. It is suggested the preference of favorable experimental conditions and application of the GGE model to identify genotypes that are more promising for stable performance across wide agroecosystems.

The expansion of conservation agriculture (CA) requires an immediate attention on targeted breeding for the next generation of wheat cultivars that are adapted to CA system and maintain their productivity under severe drought conditions in unpredictable Mediterranean environments. In this study, eight released bread and durum wheat cultivars, differing in growth habit, were evaluated for grain yield and 15 agro-physiological traits under both conventional tillage (CT) and conservation agriculture (CA) systems using split-plot arrangements in randomized complete blocks design with three replications, in three cropping seasons (2017–2020) under rainfed conditions, at Sararood field station, Kermanshah, Iran. Cultivar, year and tillage-system main effects were significant (P < 0.01) and accounted for 1.5%, 57.8%, and 10.5% of the total variation in grain yield, respectively. The wheat cultivars performed better in conventional than conservation agriculture system with grain yield superiority varied from 4 to 35%, depending on amount and distribution pattern of precipitation over crop seasons. The best performing cultivar in conservation agriculture system was cv. Baran, winter bread wheat, while the best performing cultivar under conventional tillage was cv. Rijaw, facultative bread wheat. Cv. Sadra had the highest grain yield stability followed closely by cv. Rijaw. The positive relationships observed between grain yield and important agro-physiological traits, suggesting the efficiency of indirect selection of traits for improving grain yield under the two different tillage systems. The trait profile of wheat cultivars was strongly affected by year, depending on growth habit, and varied from one system to another. In conclusion, positive interaction of genotype and traits under two systems indicated that some traits (i.e., number of spike/m−2, SPAD reading, normalized difference vegetative index (NDVI), days to heading, 1000-kernel weight and grain filling duration) were effective in genotype adaptation to a particular system. Based on the findings, the distribution pattern of precipitation during cropping season was among the major determinant factor in success of rainfed wheat productivity. The wheat genotypes also significantly interacted with different tillage systems, implying the need for specific adaptation to tillage systems and environments.

Cotton-sunflower cropping system is a unique oilseed-based rotation. The real problem is overlapping sunflower-maturity with cotton-sowing. Investigations aim to tackle cotton-late-sowing through sowing-time-adjustment and cultivar-selection at cropping-system-level. Cotton (May 10th to June 24th) and sunflower (December 20th to February 03rd) sowing-dates were maintained biweekly. Maturity-time based cultivars were selected (early, medium, late) and variations in ambient-temperature through sowing-dates from 33.2 to 33.9 °C, 32.2 to 33.6 °C and 29.2 to 32.6 °C, length of emergence-squaring, squaring-flowering and flowering-maturity differed by 3.1, 1.5 and 5.1 days, respectively. Likewise, sunflower-sowing-dates based ambient-temperature ranged 13.7–18.1 °C, 16.3–17.5 °C and 23.8–28.5 °C at emergence-budding, budding-anthesis and anthesis-maturity, resulted in a difference of 13.5, 4.8 and 1.0 days. Results revealed that cotton late-sowing (May 10th to June 24th) and sunflower (December 20th to January 19th) resulted in reduces seedcotton, lint and achene yield by 19.9, 8.2 and 8.8 kg ha−1 day−1. Oil productivity was the highest in cotton vide June 24th and in sunflower 04th January. In this cropping system, cotton is highly sensitive to sowing-dates for yield losses (35%) and sunflower less sensitive (14.4%). Meanwhile, yield variations in cotton-cultivars (12.5%) and sunflower-hybrids (10.0%). It was realized that sunflower December 20th hold great importance to assure minimum cotton yield-losses than looking for hybrids.

The planning of planting/harvest operations improves yield and economic returns of sugarcane production systems. This study aims to define homogenous regions and optimum planting dates for sugarcane using simulated water-limited yield in the state of Goiás, Brazil. Yw was simulated using crop model and 24 planting dates across the year, including gridded weather data and soil water availability to the crop over the state in a grid cell size of 0.5 × 0.5°. The crop model was evaluated comparing simulated and measured yield tendency across planting dates. Homogeneous regions were obtained based on Yw, using the Ward’s method and Euclidean distance. The crop model was able to replicate yield tendency across planting dates. The clustering divided the state into four homogenous regions, where optimum planting period had different intervals due to the interaction with climate and soil. The optimum planting window had four dates for the region with lower Yw (105 t ha−1). The region with higher Yw (131 t ha−1) had the longer optimum window, with seven dates, but with the higher yield reduction (− 6%) than other regions (− 3%) when planting date was changed from 1st to 2nd better dates. This way, the results and the approach used in this study defines yield level and optimal planting dates, which can be apply to define harvest period and the area required to supply the sugarcane mill demands, leading to better machinery and labor work management, helping to elaborate mills and state-level strategies to increase sugarcane production.