FACTORS AFFECTING WHEAT YIELD VARIATION OF SMALLHOLDER FARMERS IN PUNJAB PROVINCE: APPLICATION OF ORDERED LOGISTIC MODEL

Many socioeconomic, institutional and biophysical factors are causing high wheat yield variation among wheat growers in the country in general and in the Punjab province in specific. Ultimate purpose of present study was to determine factors affecting probability of wheat yield being in low, medium or high ordinals given the set of yield changing inputs. Cross-sectional data collected from randomly selected 320 wheat growers with 80 respondents from each of four agro-ecological regions of Punjab Province was analyzed through proportional odds model to obtain the study objectives. The study found out some socio-economic and agro-ecology related variables such as age, tractor ownership, role of income diversification through part-time farming, the contribution of smallholder tenants and owner-cum-tenants and chemical fertilizers that can significantly affect wheat yield categories of low, medium or high. Based on findings, it is imperative to support young innovative farmers having their own farm machinery, generate off-farm/on-farm income generating avenues for part-time farmers, and provide more facilities to smallholder farmers of both tenants as well as owner-cum tenant class in enhancing their wheat production of higher level. Moreover, agriculture advisory services should focus more on the cotton-wheat zone accompanied by appropriate use of seed rate, chemical fertilizers, and plant protection measures to enhance wheat yield in Punjab Province.

Single-Walled Carbon Nanotube Synthesis Yield Variation in a Horizontal Chemical Vapor Deposition Reactor

The controlled synthesis of single-walled carbon nanotubes (SWNTs) is essential for their industrial application. This study investigates the synthesis yield of SWNTs, which depends on the positions of the samples on a horizontal chemical vapor deposition (CVD) system. Methane and Fe thin films were used as the feedstock and catalyst for SWNTs synthesis, respectively. A high-resolution scanning electron microscope was used to examine the synthesis yield variation of the SWNTs along the axial distance of the reactor. The morphology and crystallinity of the fabricated SWNTs were evaluated by atomic force microscopy and Raman spectroscopy, respectively. We observed that the highest synthesis yield of the SWNTs was obtained in the rear region of the horizontal reactor, and not the central region. These results can be applied to the synthesis of various low-dimensional nanomaterials, such as semiconducting nanowires and transition metal dichalcogenides, especially when a horizontal CVD chamber is used.

Sensitivity analysis of WOFOST for yield simulation of cassava over the major growing areas of India

A study was conducted to assess the meteorological sensitivity of the WOFOST crop model in simulating the yield of cassava. The sensitivity was designed by changing the present meteorological data by ±1 to ±5 %. The results has shown the minimum temperature influencing the yield of cassava (variation: 4.94 to -7.65 %) followed by the maximum temperature (yield variation: 6.39 to -6.03 %) and solar radiation (yield variation: -2.41 to 2.07 %). The trends of these meteorological variables have been further analyzed over the major cassava growing regions in India to link its variations with cassava production. A significant trend has been detected during the monsoon season in northeast India, with a decadal change of 0.63ºC. At the same time, a significant trend was detected in the peninsular region during the winter season, with a value of 0.74ºC/decade. The rate of solar dimming in northeast India during the monsoon season was -0.53 hour/decade and during the autumn season, it was -0.25 hour/decade, respectively. The meteorological sensitivity of crop model on its yield and trends may assist the decision-makers in developing appropriate plans mitigations strategies to enhance crop production to ensure food security.

Identification of Soybean Yield QTL in Irrigated and Rain-Fed Environments

Drought is the primary abiotic stress that limits yield of soybean (Glycine max (L.) Merr.). The study aimed to identify yield-related quantitative trait loci (QTLs) in soybeans using a population of 160 F4-derived lines from ‘Hutcheson’ × PI 471938 crosses, which were cultivated under rain-fed and irrigated conditions. Seed yield was determined based on a total of nine irrigated and five rain-fed environments over two years. Twenty and twenty-seven SSR markers associated with yield (P ≤ 0.05) were identified in the irrigated and rain-fed environments, respectively. Four markers accounted for 22% of the yield variation in the irrigated environments (IR-YLD) and five markers explained 34% of the yield variation in the rain-fed environments (RF-YLD). Two independent IR-YLD and RF-YLD QTLs on chromosome (Chr) 13 (LG-F) were mapped to the Satt395-Sat_074 interval (4.2 cM) and near Sat_375 (3.0 cM), which explained 8% (LOD = 2.6) and 17% (LOD = 5.5) of the yield variation, respectively. The lines homozygous for the Hutcheson allele at the IR-YLD QTL linked to Sat_074 averaged 100 kg ha−1 higher yield than the lines homozygous for the PI 471938 allele. At two independent RF-YLD QTLs on Chr 13 and Chr 17, the lines homozygous for the PI 471938 alleles were 74 to 101 kg ha−1 higher in yield than the lines homozygous for the Hutcheson alleles. Three of the five significant SSR markers associated with RF-YLD were located in a genomic region known for canopy-wilting QTLs, in which the favorable alleles were inherited from PI 471938. The identification of yield-QTLs under the respective rain-fed and irrigated environments provides knowledge regarding differential responses of yield under different irrigation conditions, which will be helpful in developing high-yielding soybean cultivars.

Night Temperature Determines the Interannual Yield Variation in Hybrid and Inbred Rice Widely Used in Central China Through Different Effects on Reproductive Growth

Interannual variation in grain yield of rice has been observed at both farm and regional scales, which is related to the climate variability. Previous studies focus on predicting the trend of climate change in the future and its potential effects on rice production using climate models; however, field studies are lacking to examine the climatic causes underlying the interannual yield variability for different rice cultivars. Here a 6-year field experiment from 2012 to 2017 was conducted using one hybrid (Yangliangyou6, YLY6) cultivar and one inbred (Huanghuazhan, HHZ) cultivar to determine the climate factors responsible for the interannual yield variation. A significant variation in grain yield was observed for both the inbred and hybrid cultivars across six planting years, and the coefficient of variation for grain yield was 7.3–10.5%. The night temperature (average daily minimum temperature, Tmin) contributed to the yield variability in both cultivars. However, the two cultivars showed different responses to the change in Tmin. The yield variation in HHZ was mainly explained by the effects of Tmin on grain filling percentage and grain weight, while the change in spikelets m−2 in response to Tmin accounted for the yield variability in YLY6. Further analysis found that spikelets m−2 of YLY6 significantly and negatively correlated with Tmin from transplanting to heading. For HHZ, the grain filling percentage and grain weight were significantly affected by Tmin of the week prior to heading and from heading to maturity, respectively. Overall, there were differences in the response mechanism between hybrid and inbred cultivars to high night temperature. These will facilitate the development of climate-resilient cultivars and appropriate management practices to achieve a stable grain yield.

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.