Variation In Grain Yield Research Articles

Evaluation of Runoff Farming at Two Different Rainfall Zones of the Semiarid Climate of Erbil Province

Rainfed agriculture in arid and semiarid regions is risky due to low rainfall and uneven distribution. To improve crop productivity runoff water can be utilized as alternative source of available water. Two field experiments were conducted at Chalook and Byok to study the interactive effect of five levels of catchment cultivation area ratio (0,1,2,3 and 4) and two catchment slopes (5% and 10%) on wheat growth and yield,15 runoff plots were established at each site and for each slope. Lower part of each plot served as a cultivated area, with dimensions of 2.5 m x 3 m and kept nearly flat. Conversely the upper part of each plot served as scarified catchment, all of the same width of 2.5 m, but of different lengths to offer catchment cultivated area ratios of 0,1,2,3, and 4. Results showed a gradual increase in wheat grain yield and aboveground biomass with an increase in catchment to cultivated area ratio (CA:C) at both sites. However, the catchment slope produced a higher grain yield compared to 10% slope, but the difference was insignificant. The theoretical CA:C values compared to the maximum applied ratio suggest the possibility of further increase in grain yield with an increase beyond 4:1, particularly at Chalook site. Byok site outperformed the Chalook site due to increased water arability at the former site. Linear regression analysis revealed that CA:C merged as the most effecting factor affecting grain yield, followed by annual rainfall. Over 91% of variation in grain yield can be assigned to variations in CA:C, annual rainfall, and catchment slope.

Influence of small-scale spatial variability of soil properties on yield formation of winter wheat

BackgroundWith the increasing development of sophisticated precision farming techniques, high-resolution application maps are frequently discussed as a key factor in increasing yield potential. However, yield potential maps based on multiple soil properties measurements are rarely part of current farming practices. Furthermore, small-scale differences in soil properties have not been taken into account.MethodsTo investigate the impact of soil property changes at high resolution on yield, a field trial has been divided into a sampling grid of 42 plots. The soil properties in each plot were determined at three soil depths. Grain yield and yield formation of winter wheat were analyzed at two sites.ResultsMultiple regression analyses of soil properties with yield measures showed that the soil contents of organic carbon, silt, and clay in the top and subsoil explained 45–46% of the variability in grain yield. However, an increasing clay content in the topsoil correlated positively with grain yield and tiller density. In contrast, a higher clay content in the subsoil led to a decrease in grain yield. A cluster analysis of soil texture was deployed to evaluate whether the soil´s small-scale differences caused crucial differences in yield formation. Significant differences in soil organic carbon, yield, and yield formation were observed among clusters in each soil depth.ConclusionThese results show that small-scale lateral and vertical differences in soil properties can strongly impact crop yields and should be considered to improve site-specific cropping techniques further.

Genotype and agronomic management interaction to enhance wheat yield and water use efficiency in the Mediterranean rainfed environment of Morocco: I. Field data analysis

Durum wheat (Triticum turgidum subsp. durum), considered better drought tolerant, is the most cultivated wheat in Morocco and in the Middle East and North Africa (MENA) region. The region, including Morocco, predominantly has rainfed production systems, declining water supply, and increasing trends and effects of rainfall variability and climate extremes leading to poor crop yield and yield stability. The objectives of this study were to understand major factors determining wheat yield and water use efficiency (WUE); assess the interaction of genotype × environment × management on crop yield and field- and crop-water use efficiencies; and determine the water-limited yield gaps of genotypes under different agronomic management practices in the Mediterranean climate of Morocco. Four years (2015/16–2018/19) of on-station experiments investigating genotype (ten genotypes including seven advanced line and three commercial varieties), seeding time (17 November vs. 29 December), and water management (rainfed vs. supplementary irrigation), was conducted at Merchouch research station, Morocco. The results showed that durum wheat yield mostly varied due to year (rainfall) by 43%, followed by water management × year (23%), year × seeding time (15%), and genotype (7%), with the highest yield (7.15 t ha−1) observed in 2018 (wet year) and the lowest in 2019 (dry year). Across years, not only the rainfall amount but also its distribution during the crop growing season caused yield variability. In low-rainfall years, supplementary irrigation (28–166 mm) increased yield by 2.12–3.27 t ha−1 compared to rainfed conditions. The significant (p < 0.01) year × seeding time × genotype effect on grain yield and WUEs indicates that the response of genotype and seeding time varied with rainfall amount and distribution as the water-limited yield gap in rainfed conditions appeared to be more than 2 t ha−1. In both supplementary irrigated and rainfed systems, the machine learning model showed the effect of different climatic (rainfall and temperature) and management (seeding time, genotype, and irrigation) factors determining yield, yield attributes, and WUEs. The total rainfall remained the most important factor explaining variation in rainfed wheat yield followed by the temperature at flowering, mid- and early-season rainfall, and genotype in order of importance. Similarly, seasonal rainfall was the most important factor explaining variation in grain yield followed by early-season rainfall, days to flowering, temperature at flowering, irrigation amount, seeding time, and genotype in supplementary irrigated durum wheat. Our study clearly shows that yield is highly related to rainfall in rainfed drylands, and the selection of high WUE variety and crop management practices are the key to improving the resilience and sustainability of durum wheat in Morocco and similar production environments in the MENA region.

Genomic regions of durum wheat involved in water productivity.

Durum wheat is a staple food in the Mediterranean Basin, mostly cultivated under rainfed conditions. As such, the crop is often exposed to moisture stress. Therefore, the identification of genetic factors controlling the capacity of genotypes to convert moisture into grain yield (i.e., water productivity) is quintessential to stabilize production despite climatic variations. A global panel of 384 accessions was tested across 18 Mediterranean environments (in Morocco, Lebanon, and Jordan) representing a vast range of moisture levels. The accessions were assigned to water responsiveness classes, with genotypes 'Responsive to Low Moisture' reaching an average +1.5 kg ha-1 mm-1 yield advantage. Genome wide association studies revealed that six loci explained most of this variation. A second validation panel tested under moisture stress confirmed that carrying the positive allele at three loci on chromosomes 1B, 2A, and 7B generated an average water productivity gain of +2.2 kg ha-1 mm-1. These three loci were tagged by kompetitive allele specific PCR (KASP) markers, and these were used to screen a third independent validation panel composed of elites tested across moisture stressed sites. The three KASP combined predicted up to 10% of the variation for grain yield at 60% accuracy. These loci are now ready for molecular pyramiding and transfer across cultivars to improve the moisture conversion of durum wheat.

Effects of Salt Stress on Grain Yield and Quality Parameters in Rice Cultivars with Differing Salt Tolerance.

Rice yield and grain quality are highly sensitive to salinity stress. Salt-tolerant/susceptible rice cultivars respond to salinity differently. To explore the variation in grain yield and quality to moderate/severe salinity stress, five rice cultivars differing in degrees of salt tolerance, including three salt-tolerant rice cultivars (Lianjian 5, Lianjian 6, and Lianjian 7) and two salt-susceptible rice cultivars (Wuyunjing 30 and Lianjing 7) were examined. Grain yield was significantly decreased under salinity stress, while the extent of yield loss was lesser in salt-tolerant rice cultivars due to the relatively higher grain filling ratio and grain weight. The milling quality continued to increase with increasing levels. There were genotypic differences in the responses of appearance quality to mild salinity. The appearance quality was first increased and then decreased with increasing levels of salinity stress in salt-tolerant rice but continued to decrease in salt-susceptible rice. Under severe salinity stress, the protein accumulation was increased and the starch content was decreased; the content of short branched-chain of amylopectin was decreased; the crystallinity and stability of the starch were increased, and the gelatinization temperature was increased. These changes resulted in the deterioration of cooking and eating quality of rice under severe salinity-stressed environments. However, salt-tolerant and salt-susceptible rice cultivars responded differently to moderate salinity stress in cooking and eating quality and in the physicochemical properties of the starch. For salt-tolerant rice cultivars, the chain length of amylopectin was decreased, the degrees of order of the starch structure were decreased, and pasting properties and thermal properties were increased significantly, whereas for salt-susceptible rice cultivars, cooking and eating quality was deteriorated under moderate salinity stress. In conclusion, the selection of salt-tolerant rice cultivars can effectively maintain the rice production at a relatively high level while simultaneously enhancing grain quality in moderate salinity-stressed environments. Our results demonstrate specific salinity responses among the rice genotypes and the planting of salt-tolerant rice under moderate soil salinity is a solution to ensure rice production in China.

IRON-ZINC BASED GENETIC DIVERSITY ASSESSMENT IN MAIZE (ZEA MAYS L.) GENOTYPES

Hidden hunger is one of the most important challenges of the current era, and genetic biofortification is the most feasible, cheapest, and sustainable way to provide a balanced diet to the community. Given the value of biofortification in food grains, the relevant study sought to screen maize inbred lines for kernel Fe and Zn contents and estimate their bioavailability using molar ratios. One hundred maize inbred lines planted during spring 2018 in soil contained optimal levels of Fe and Zn. Maize genotypes evaluation comprised plant height, days to tasseling, silking, maturity, cob length, number of rows per cob, grains per row, grains per cob, 100-grain weight, grain yield per plant, grain Fe, Zn, and phytic acid contents. Significant differences emerged for all the studied traits. The results of the correlation study indicated that grain Fe and Zn contents had a positive genetic link with each other while a non-significant negative association with phytic acid and grain yield. A substantial positive correlation of grain yield occurred with rows per cob, grains per row, and grains per cob. Cluster and principal component analyses ran through, with PA/Fe and PA/Zn molar ratios calculated to estimate the mineral bioavailability. Based on the genetic variability for grain yield, Fe, Zn, and PA contents, four clusters resulted, and the first two PCs had an eigenvalue of more than one and depicted 76.91% of the total variance. Genotypes M-11, M-41, M-45, M-56, M-60, M-61, M-66, M-80, M-96, and M-98 showed high Fe and Zn contents with low molar ratios and are potential to benefit further breeding programs to develop biofortified maize hybrids.

Targeting resilient lentil genotypes with an adding value of nutritional quality by using AMMI and GGE biplots analysis

The current study aims to assess the impact of different genotypes, environmental conditions, and their interactions (G×E) on lentil yield and nutritive traits in various agro-ecological locations across Morocco. To achieve this, two analysis methods, Analysis of Main Additive Effects and Multiplicative Interaction (AMMI), and Genotype and Genotype by Environment (GGE) were used. The study involved evaluating sixty-four lentil genotypes in six diverse environments during the 2017–2018 and 2019–2020 seasons. Results from the analysis of variance revealed that environmental variation significantly influenced grain yield (75.7%), zinc (48.4%), and magnesium (73.3%). In contrast, genotype by environment interaction (G×E) played a more substantial role in determining protein (45.7%), iron (53.2%), and manganese (49.6%) content. The first two components explained 69.2%, 78.3%, 90.5%, 79.3%, 71.4%, and 74.3% of the variation in grain yield, protein content, iron, zinc, manganese, and magnesium, respectively. The GGE biplot analysis identified specific environments (E3 and E5) as representative and discriminative for yield, zinc, and manganese. Similarly, E3 and E4 were discriminative for iron and protein and magnesium, respectively. Seventeen lentil genotypes exhibited high performance, combining yield and nutritional quality. Notably, genotypes LN34 and VR28 performed well in the Marchouch 2019-2020 environment, while genotype LN54 excelled in the Douyet and Sidi el Aydi environments during 2019-2020. Furthermore, three advanced lines (LN34, LN58 and LN64) expressed stability in yield and most nutrient traits, outperforming released lentil varieties. These promising lines hold potential for developing novel, resilient lentil varieties with both high yield and nutritive quality.

Genetic variability and agronomic performances of rice genotypes in different growing seasons in Bangladesh

Genetic variability is a key factor in the selection of suitable genotypes in rice breeding programs. To evaluate the genetic variability of 11 rice genotypes, a study was carried out in the field of agronomy at Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, during 2019–2020. These genotypes were characterized for 11 traits and analyzed to determine the level of genetic and agronomic diversity as well as the degree of association existing between grain yield and its related component traits. The results revealed significant differences (P ≤ 0.001 and 0.05) among the rice genotypes for the studied traits in all growing seasons. Furthermore, it was noted that the phenotypic coefficient of variation was more pronounced than the genotypic coefficient of variation, highlighting the environmental impact on each trait. The majority of the traits had moderate to high heritability and genetic advance across the three seasons, showing additive gene action. The principal component analysis showed that the first three principal components accounted for the greatest variability, with the majority of the evaluated traits significantly influencing the genetic variability. The mean value of studied traits exhibited that there was seasonal variation of grain yield by the genotypes, such as BU-R-ACC-08 and BU-R-ACC-11 genotypes, which produced higher yield in Aus, BU-R-ACC-05 and BU-R-ACC-06 in Aman, and BU-R-ACC-07 and BU-R-ACC-06 in Boro season, where several genotypes mature earlier in all growing seasons, viz., BU-R-ACC-05, BU-R-ACC-01, BU-R-ACC-10, and BU-R-ACC-11. The correlation analysis revealed that there were significant, strong positive or negative correlations among the traits. The results suggest that agronomical traits that were positively correlated with grain yield could be useful in selecting desired rice genotypes.

Deciphering Variability and Genetic Parameters in Traditional Scented Rice (Oryza sativa L.) Genotypes of Kerala, India

Gandhakasala and Jeerakasala, the most popular traditional scented rice cultivars of Wayanad district in Kerala were used to assess the nature and magnitude of variability and genetic parameters in this investigation. The study on twenty yield and associated quantitative, cooking and biochemical characters revealed significant differences among genotypes for each character except for leaf width indicating the presence of high genetic variability among the genotypes. Phenotypic and genotypic coefficient of variations showed high level of variability for grain yield (42.19%), number of spikelets panicle-1 (31.36%) and test weight (26.23%) and moderate level of variability for leaf length, culm number, culm length, number of productive tillers, number of spikelets panicle-1, panicle length, straw yield, grain length, grain width, amylose content and protein content. For most of the characters studied, the difference between GCV and PCV was narrow, indicating that environmental factors have little influence on these characters and are highly influenced by genetic factors. Heritability estimations ranged from low to high (17.86% to 99.90%). Characters like leaf length, culm number, number of spikelets panicle-1 and straw yield showed high heritability along with high genetic advance reflecting that these characters were under additive gene action and these could be the desirable characters for effective selection for developing high yielding scented rice varieties. This study provides information about the genetic variability and heritability measures of the various yield and related characters which may help to establish the selection criteria for future improvement in short grain scented rice breeding programmes.

Estimating grain and biomass yield of bread wheat genotypes by optical sensors

Use of optical sensors in wheat breeding programs can provide convenience in predicting the grain and biomass yield. In this study, optical sensors were used to estimate the grain and biomass yield of different wheat genotypes. Field trials were conducted with 49 wheat genotypes in three different WST (full irrigation (FI), supplementary irrigation (SI), and rainfed) in Central Anatolia (Eskisehir Province) in the 2011 and 2012 growing seasons. The genotypes were significantly different in grain yield, biomass weight (BM), LAI, and NDVI, SPAD ZD 55 (SPAD measured on ZD 55) was the most critical variable to predict grain yield, it was significantly correlated with grain yield in all three water supply conditions. Similarly to SPAD ZD 55, NDVI ZD 31, and LAI ZD 41 were the most significant attributes to predict BM in all three water supply cases. Multiple linear regression equation developed using independent variables of SPAD ZD 55, NDVI ZD 60, NDVI ZD 31, and NDVI ZD 24 described 52 % of the total variation in grain yield of 49 genotypes. The genotypes were highly consistent in the relationship between grain yield and SPAD ZD 55 and the relationship between BM and LAI ZD 41 in all three water supply cases. The results were promising to predict grain yield of highly different wheat genotypes under different water supply regimes. However, more research is needed to conceptualize relationships between yield and sensor measured variables in wheat genotypes across different environments and soil settings.

Grain yield stability analysis using parametric and nonparametric statistics in oat (Avena sativa L.) genotypes in Ethiopia

AbstractBackgroundThe performance of oat genotypes differs across environments due to variations in biotic and abiotic factors. Thus, evaluation of oat genotypes across diverse environments is very important to identify superior and stable genotypes for yield improvement.MethodsThe study aimed to assess the interaction (genotype‐by‐environment interaction; GEI) effect and determine the stability of grain yield in oat (Avena sativa L.) genotypes in Ethiopia using parametric and nonparametric stability statistics. Twenty‐four oat genotypes were evaluated in nine environments using a randomized complete block design replicated three times.ResultsThe pooled analysis of the variance of grain yield showed significant variations among genotypes, environments, and their interaction effects. Significant GEI revealed the rank order change of genotypes across environments. The environment main effect captured 44.62% of the total grain yield variance, while genotype and GEI effects explained 28.84% and 26.54% of the total grain yield variance, respectively. The grain yield stability was assessed based on 12 parametric and two nonparametric stability statistics. The results indicated that genotypes with superior grain yield‐ showed stable performance on the basis of the stability parameters of the genotypic superiority index (Pi), the Perkins and Jinks adjusted linear regression coefficient (Bi), and the yield stability index (YSI), indicating that selection using these stability parameters would be efficient for grain yield enhancement in oat genotypes. Spearman's rank correlation coefficients also showed that the stability parameters of Pi, Bi, and YSI had a significant positive association with grain yield. However, grain yield had an inverse correlation with the stability parameters of standard deviation, deviation from regression , the Hernandez desirability index (Dji), Wricke ecovalence (Wi), the Shukla stability variance (σi2), the AMMI stability value (ASV), and environmental variance , indicating that oat genotype selection using these stability parameters would not be efficient for yield enhancement because these stability parameters favor low‐yielding genotypes more, compared to high‐yielding ones.ConclusionsTherefore, G5, G8, G11, G12, G14, G16, G17, G19, and G22 genotypes were adaptable in all nine environments based on stability parameters of Pi, Bi, and YSI, and selection of these superior genotypes would improve grain yield in oat genotypes. However, the validity of this result should be confirmed by repeating the experiment in the same environments over two or more years.

Application of multivariate analysis for assessment of stability in winter safflower (Carthamus tinctorius L.) genotypes under rainfed conditions

Assessment of genotype x environment interaction (GEI) is an important constituent of cultivar selection process in multi-environment traits (MET). A set of 15 genotypes of winter safflower (Carthamus tinctorius L.) were evaluated for grain yield, compatibility, yield stability and GEI in diverse environments under rainfed conditions. The analysis of variance conducted through AMMI method showed significant variation in grain yield with respect to mean squares of environments, genotypes, and GEI. The two main components, IPCA1 and IPCA2, accounted for 85.29% of the variation and GEI, indicating almost high biplot validity. The polygon biplot analysis identified six superior and stable genotypes and two mega-environments

Growth and Productivity of Cowpea (Vigna unguiculata L.) in Response to Seed Priming and Different Levels of Phosphorus

Acidic soil generally has lower availability of phosphorus. Grain legumes respond positively to higher levels of phosphorus in acidic soil. Seed priming also enhances growth of the crops by activating various metabolic processes in plants. A field experiment was carried out to determine the influence of seed priming and phosphorus levels on growth and productivity of cowpea at Bhojad, Chitwan, Nepal. The experiment plot was laid out in two factorial randomized complete block design with three replications and the planting was done in spring season of 2022. The treatment consisted of two levels of seed priming (no priming and priming with 0.2 % Ca (NO3)2 for 24 hours) and four levels of P2O5 (20,30,40 and 60 kg ha-1). The variations in different parameters of cowpea due to seed priming were significant (P &lt; 0.05). Primed seed produced the highest grain yield (1.25 t ha-1), which was followed by non-primed seeds (1.16 t ha-1). Similar significant (p&lt;0.001) differences in yield and yield-attributing (no. of grains/pods, test weight, biological yield, grain yield and harvest index) traits were found at different levels of P2O5, with the highest grain yield (1.44 t ha-1) was obtained when applied with 40 kg P2O5 ha-1. Significant (p&lt;0.001) variations in grain yield were found as a result of the interaction between priming and P2O5 levels. The combination of priming and 40 kg P2O5 ha-1 produced the highest grain yield (1.51 t ha-1). The results showed that the use of primed seed with 40 kg P2O5 ha-1 would be beneficial for the farmers in the Terai region of Nepal.

Linear mixed model to identify the relationship between grain yield and other yield related traits and genotype selection for sorghum

Sorghum is the most popular crop in arid and semi-arid areas, especially in Sub-Saharan African countries. Genotype effects, environmental and the interaction of genotype by environmental factors have an influence on phenotypic traits. The aim of the study is to identify the relationship between grain yield and other yield-related traits and select the genotypes which perform better in grain yield as well as to examine the association between the uncorrelated phenotypic traits and grain yield via mixed model. The data was generated using a lattice square design. Principal component analysis was used to generate uncorrelated variables for the mixed model. The study revealed that there was a difference in grain yield due to the treatment and there was a pairwise relationship among the phenotypic variables. 77.12% of the total variance of the original phenotypic variables was explained by the first three principal components and decided to use PCAs as input variables for the mixed model. All PCs had significant effects on grain yield as well as grain yield variability due to random effects associated with genotypes, genotype interaction by treatment, and replication within the treatment. The variability of grain yield due to genotype effect was explained about 45.73%, the variation of grain yield due to the interaction of genotype by the treatment was also explained about 39.06% and 1.55% of the variation of grain was explained by replication within treatment. The best performer genotypes recommended for mass production were G40 (Genotype 40), G186 (Genotype 186) and G196 (Genotype 196) without any constraint of environment. The genotypes recommended for mass production under irrigation conditions were G40 (Genotype 40), G62 (Genotype 62) and G192 (Genotype 192). G26 (Genotype 26), G55 (Genotype 55) and G49 (Genotype 49) were the genotypes recommended for mass production under stress conditions. Overall, the study recommends using a mixed model to fit the grain yield, and future work will focus on to evaluate the performance of genotypes under different environments and years of production.

Selection of High-Yielding and Stable Genotypes of Barley for the Cold Climate in Iran.

The interaction between genotypes and environments plays an important role in selecting superior genotypes for target locations. The main objectives of the present study were to analyze the effect of the genotype-by-environment interaction (GEI) and identify superior, newly developed, and promising barley genotypes for cold regions in Iran. For these purposes, a set of genotypes obtained from breeding programs for cold climates in Iran, along with two reference genotypes, were investigated at eight research stations (Tabriz, Ardabil, Arak, Miandoab, Mashhad, Jolge Rokh, Karaj, and Hamadan) during two consecutive growing seasons (2019-2020 and 2020-2021). The results of the freezing test (LT50) showed that most of the tested genotypes had significant cold tolerance at the seedling stage. Based on the additive main effect and multiplicative interaction (AMMI) analysis, environment (E) and GEI effects explained 49.44% and 16.55% of the total variation in grain yield, respectively. Using AMMI1 and AMMI2 models, G2 and G20 were found to be superior genotypes in terms of grain yield and stability. Moreover, AMMI-based stability parameters considered the G20 genotype to be the ideal genotype. A two-plot analysis of the genotype-by-environment interaction (GGE) biplot showed that the 16 experimental environments were grouped into 2 mega-environments. Of the test environments, ARK1 and KAJ2 had the highest discriminating power and representativeness ability, and these were identified as ideal environments for testing advanced genotypes for yield and stability performance during early barley breeding practices in cold areas in Iran. In conclusion, both AMMI and GGE biplot models identified several superior genotypes, among which G20, with a high average yield relative to the overall average yield and the lowest IPC1 score, was found to have high yield stability and is recommended for inclusion in breeding programs for cold climates in Iran.

Leaf reflectance characteristics and yield of spring oat varieties as influenced by varietal divergences and nutritional supply

Inadequacy of nutrients in the soil and sufficient inaccessibility to nutrients is caused by factors that affect production and productivity of spring oat varieties. Exogenous application of nutrient and real time nutrient assessment can therefore reverse these associated negative consequences. A field experiment was conducted to evaluate the response of spring oat varieties to sulphur and silicon based fertilisation. Eight spring oat varieties, four level of nutrient application was arranged in a split-plot design with three replications. The obtained results showed that foliar application of sulphur improves the grain yield of most nutrients responsive varieties by about 34.7%. However, application of silicon had shown a diminishing return association to grain yield of variety GK Kormorán, GK Pillangó, Lota, Panni. LAI, thousands grain weight, SPAD, NDVI was significantly (p &lt; 0.05) influenced by genetic difference of the tested varieties, developmental plasticity, and nutrient application. Significantly higher grain yield was obtained from the variety Mv Pehely than the other tested varieties. Therefore, it could be inferred that a combined use of nutrient responsive spring oat varieties and sulphur containing fertilisers could be important agronomic practice to improve grain yield and to develop climate resilient oat varieties.

Temporal effects of climate and soil fertility‐mediated maize yield and its sustainability: A case study in subtropical China

AbstractThe sustainability of food production systems requires accurate assessments of the combined impacts of climate change and soil fertility on crop yield. However, a knowledge gap remains regarding the interactions of climate and soil fertility for the determination of crop yield and sustainability. For this study, we investigated how climate and soil fertility affected maize yield and its sustainability over a 30‐year experiment on red soil. Seven fertilization treatments were selected (CK (no fertilizer), N (chemical nitrogen), NP (chemical nitrogen and phosphorus), NK (chemical nitrogen and potassium), NPK, M (pig manure), and NPKM (NPK and M)). The link between grain yield and the sustainability yield index (SYI) increased exponentially (p &lt; 0.001). Manure (M and NPKM) improved the concentrations of soil nutrients considerably compared to chemical fertilizer treatments (N, NP, NK, and NPK). The explanatory rates of selected climatic and soil parameters for grain yield and its SYI varied from 56.6% to 66.9%. Soil available nutrients (AP and AK) and total nutrients (TN and TP) regulated grain yield and the SYI at low and high soil fertility, respectively. Redundancy analysis showed a negative correlation between temperature throughout the maize growing season, grain yield, and the SYI, excluding the NPKM treatment. Correlations between temperature, grain yield, and the SYI were reduced as soil fertility increased. Climate (44.0%) and nutrient inputs (40.2%) under chemical and manure fertilization contributed the most to the observed yield. Path analysis explained 68% and 77% of the variations in grain yield under chemical fertilizers and manure treatments, respectively. Additionally, the results suggested that as air temperatures are predicted to rise early maize should be planted earlier and late maize should be planted later to optimize grain yield. Together, this study emphasized that improving soil quality and adjusting planting schedules in agricultural management practices can attenuate the negative impact of climate warming on grain yield sustainable production in the global subtropical regions.

Comparison of PlanetScope, Sentinel-2, and landsat 8 data in soybean yield estimation within-field variability with random forest regression

Accurate timely and early-season crop yield estimation within the field variability is important for precision farming and sustainable management applications. Therefore, the ability to estimate the within-field variability of grain yield is crucial for ensuring food security worldwide, especially under climate change. Several Earth observation systems have thus been developed to monitor crops and predict yields. Despite this, new research is required to combine multiplatform data integration, advancements in satellite technologies, data processing, and the application of this discipline to agricultural practices. This study provides further developments in soybean yield estimation by comparing multisource satellite data from PlanetScope (PS), Sentinel-2 (S2), and Landsat 8 (L8) and introducing topographic and meteorological variables. Herein, a new method of combining soybean yield, global positioning systems, harvester data, climate, topographic variables, and remote sensing images has been demonstrated. Soybean yield shape points were obtained from a combine-harvester-installed GPS and yield monitoring system from seven fields over the 2021 season. The yield estimation models were trained and validated using random forest, and four vegetation indices were tested. The result showed that soybean yield can be accurately predicted at 3-, 10-, and 30-m resolutions with mean absolute error (MAE) value of 0.091 t/ha for PS, 0.118 t/ha for S2, and 0.120 t/ha for L8 data (root mean square error (RMSE) of 0.111, 0.076). The combination of the environmental data with the original bands provided further improvements and an accurate yield estimation model within the soybean yield variability with MAE of 0.082 t/ha for PS, 0.097 t/ha for S2, and 0.109 t/ha for L8 (RMSE of 0.094, 0.069, and 0.108 t/ha). The results showed that the optimal date to predict the soybean yield within the field scale was approximately 60 or 70 days before harvesting periods during the beginning bloom stage. The developed model can be applied for other crops and locations when suitable training yield data, which are critical for precision farming, are available.

Variability of Cost and Return of Dry Direct Seeded Lowland Rice in Major Rainfed Growing Areas in the Philippines

This study was conducted to evaluate the variability of net income and return on investment (ROI) as affected by grain yield, cost of production, and gross income across provinces wherein the mechanized dry direct seeding technology was being deployed. The study was conducted during 2019 wet season involving 54 sites from 14 provinces with farmers practicing mechanized dry direct seeding technology under rainfed lowland condition. Grain yield varied (0.72 – 6.73 t ha-1) across provinces and within each province. Total cost of production varied (PhP 23032 – 43421 ha-1) across provinces. Most sites in Cagayan had high cost of production while low in Negros Occidental. Fertilizer cost was the major expense of more than 50% of rice farmers across provinces. Gross income ranged from PhP 32939 ha-1 (South Cotabato) to PhP 73873 ha-1 (Abra). Highest net income was obtained in Sultan Kudarat due to high yields. Net income was less variable in Ilocos Sur while highly variable in South Cotabato. The return on investment (ROI) of different provinces varied (71.61 ± 62.63%). Low ROI and less variability were noted in Ilocos Norte, Ilocos Sur, and La Union. Across provinces, 60.7% of total variation was explained by variations in grain yield. About 50% of variations in ROI was associated with cost due to land preparation and pesticide application. Fertilizer cost accounted for the high ROI in Abra and Antique. The cost of harvesting significantly affected ROI in Albay, Antique, Cagayan, Camarines Sur, Ilocos Norte, Ilocos Sur, and South Cotabato. In general, variability in ROI across provinces were affected by grain yield levels, or the total or the specific items in the cost of production, which is site or province specific. From the information generated, interventions can be formulated based on identified limiting factors specific to site or province.

Determination of Drought Tolerance Indices as Selection Criteria of Rice Genotypes under Water Deficit Conditions in Egypt

The development of rice genotypes with impressive tolerance to abiotic stress is one of the primary objectives of rice breeding programs. The current study was carried out to assess the capability of different indices to identify rice genotypes that are drought-tolerant under Egyptian conditions during the two growing seasons 2018 and 2019. Thirteen drought tolerance indices were calculated using the grain yield of twenty rice genotypes. According to the combined analysis of variance for grain yield, there were highly significant differences between genotypes (G), irrigation regimes (I), and their interactions. All drought tolerance indices, as well as grain yields under non-stress and stress conditions (Yp and Ys), exhibited significant variations among genotypes. For Yp, Ys, and all indices assessed, estimates of broad sense heritability (h2) and genetic advance as a percentage of mean (GAM%) were high. Using mean performances, drought tolerance indices, and multivariate analysis, the genotypes Giza 179, Sakha 104, IET 1444, and GZ 6296-12-1-2-1 were superior genotypes under both stress and non-stress conditions, as were the genotypes Sakha 107, IRAT 170, IR 68552-55-3-2, WAB 56-104, IRGA 318-11-6-2-6, and NERICA -4 under stress conditions. Hence, it was suggested that they can be utilized as parents in hybridization programs that aim to improve the drought tolerance of other rice genotypes in Egypt. Furthermore, mean productivity index (MP), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HM), and yield index (YI) were the tolerance indices that can be classified as better predictors of drought tolerance considering the yield potentials of the genotypes.