Seasonal Yield Research Articles

Crop Water Stress Index and Yield Relationships for Winter Wheat (Triticum aestivum) Crops Grown Under Different Drip and Flood Irrigated Treatments

ABSTRACTThe Crop Water Stress Index (CWSI) is a widely used method for quantifying crop water status and predicting yield. However, its evaluation across different irrigation methods and its stage‐specific response to crop yield is rarely evaluated. In this study, controlled field experiments were conducted on winter wheat using drip irrigation (DI) and flood irrigation (FI) during the 2021–2022 and 2022–2023 seasons in western Uttar Pradesh, India. The irrigation treatments included 50% MAD (maximum allowable depletion) (DI), 55% MAD (DI), 60% MAD (DI), 50% MAD (FI), local farmer's field replication (FI), rain‐fed, and well‐watered treatment (DI). The derived mean CWSI values for the irrigation treatments ranged from 0.03 to 0.66 in season 1 and 0.06 to 0.57 in season 2 across treatments. The seasonal mean CWSI for 50% MAD (DI) was 0.12 (season 1) and 0.11 (season 2), while 50% MAD (FI) yielded higher mean CWSI values of 0.29 (season 1) and 0.22 (season 2). The 50% MAD (DI) treatment produced the highest grain yield and water use efficiency in both seasons. A comprehensive analysis of stage‐specific CWSI values and grain yields revealed that grain yield was more sensitive to post‐heading CWSI as compared to pre‐heading CWSI values. Among the growth stages, CWSI values during the flowering stage were the most critical for predicting wheat yield. The study recommends that the CWSI values in the flowering and post‐heading stages are more relevant in predicting wheat yield accurately as compared to the pre‐heading and seasonal mean CWSI.

Predictive Cultivation: Integrating Meteorological Data and Machine Learning for Enhanced Crop Yield Forecast

Agriculture is a key component of Telangana’s economy, and greater performance in this sector is crucial for inclusive growth. A central challenge is yielding estimation to predict crop yields before harvesting. This paper addresses this challenge with machine learning approaches includes Naive Bayes, KNN and Random Forest. The parameters considered for model testing are crop, season, rainfall and location. This paper includes a case study of Telangana with the help of Telangana weather data set to provide analysis on the key factors like overall rainfall recorded with respect to each Mandal, overall seasonal yield in selected years, seasonal yield of major crops like Bengal gram, groundnut and maize, and overall yield in two different agricultural seasons: rabi and kharif. Random forest machine learning model produces highest accuracy of 99.32% when compared with other process models.

Leveraging crop yield forecasts using satellite information for early warning in Senegal

Agricultural losses driven by climate variability and anthropogenic pressures have severely impacted food security in Senegal. There is a crucial need to generate early warning signals for the upcoming season to enhance food security in response to the sudden climate shocks like drought. In this study, we investigated the spatial distribution of maize and groundnut using factor analysis with a principal component approach. We aimed to identify suitable predictors of crop yields for the development of a seasonal yield prediction model. Subsequently, multi-regression analysis was performed to predict crop yield based on various combinations of satellite-derived vegetation and climate (rainfall) datasets as well as agronomic data from Senegal's 40 districts between 2010 and 2021. Studies revealed a strong correlation between seasonal rainfall (May to September) and crop yield: a 10–20% decline in rainfall can lead to crop losses. The accuracy of the yield prediction model, built on the best performing scenarios for each district based on monsoon onset, duration, and planting time, exceeded 0.5 (R-squared) for all districts when combining rainfall and normalized difference vegetation index (NDVI) data. The model prediction accuracy varied between 0.6 and 0.8 for major crop growing areas. The study emphasizes that refining the yield prediction model using machine learning techniques can improve its accuracy and enable its implementation in early warning systems. This enhanced capability could bolster Senegal's resilience to climate change by aiding decision-makers and planners in developing more effective strategies to ensure food security.

Grain productivity and nutrient accumulation in millet straw grown in the off-season

Given the growing demand for grains cultivation in the off-season, an experiment was conducted to evaluate grain yield and nutrient accumulation in straw, straw production, and decomposition of pearl millet varieties and hybrids cultivated at different sowing times in off-season in the municipality of Rio Verde, state of Goiás. This was a randomized block experimental design, with four replications and four planting times. The work was carried out on an Oxisol, using two forage varieties, ADR300 and ADR500, the dual-purpose pearl millet hybrid ADR8010 for grain and biomass production, and two grain hybrids, ADR9010 and ADR 9020. The accumulation of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium and sulfur) was quantified in the straw at grain harvest. Hybrids ADR 9010 and ADR 9020 had the highest grain yield in the first season, with 2,626 and 2,425 kg ha-1, respectively, while the ADR 9010 hybrid showed the highest accumulation of macronutrients (except phosphorus) in the straw. Hybrid ADR 9010 is the best option for planting in the off-season with the objective of grain production, with high biomass production and accumulation of nutrients in the straw.

Balancing Yield and Environmental Impact: Nitrogen Management and Planting Density for Rice in Southwest China

With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m−2 (traditional density, D1) and 20 hills m−2 (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha−1 (N1), and 144 kg N ha−1 (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha−1, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p < 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p < 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg−1, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFPN). The negative correlation between the NAE and PFPN with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.

Characterization of Improved Barley Germplasm under Desert Environments Using Agro-Morphological and SSR Markers

Barley is indeed a versatile cereal crop, valued for its uses in food, animal feed, and increasingly in biofuel production. As interest grows in developing new barley genotypes that are better adapted to diverse environmental conditions and production systems, integrating agro-morphological evaluations with molecular marker analyses in barley breeding programs is essential for developing new genotypes. It is necessary to explore the genetic diversity of those germplasm to predicate their responses to targeted environments and regions. The current study explored the phenotypic and genotypic relations among Saudi advanced germplasm to facilitate the development of superior barley cultivars suitable for desert environments. Molecular microsatellites (SSR) markers revealed considerable wide genetic variation among Saudi germplasm and checks. Population structure analyses revealed four main groups. Those groups were validated using similarity analyses and coefficients. As well, principal components analysis (PCA) and heat map analyses separated the studied genotypes into four main groups. The improved Saudi germplasm, selected from the barley breeding program, revealed considerably wide genetic and phenotypic diversities, indicating the feasibility of selection to improve for semi-arid conditions. The improved line KSU-BR-C/G-2 had the highest grain yield and harvest index in the first season. Rihana/Lignee was followed by the KSU-BR-C/G-2 genotype, with a grain yield averaging 6734.07 (kg ha−1), in the first season. KSU-BR-88-29-10 yielded 20,000 kg ha−1 for biomass yield. In the second year, KSU-BR-30-7 had the highest biomass yield, with 27,037.04 kg ha−1.

Optimizing sowing dates and nitrogen levels for enhanced maize yield and growth in kharif seasons over Terai region of Uttarakhand

Optimizing sowing dates and nitrogen levels for enhanced maize yield and growth in kharif seasons over Terai region of Uttarakhand

Synergistic Effects of Microbial Gibberellic Acid and Vitamins on Onion (Allium cepa L.) Yield, and Quality in Low-Fertility Soil

Onion is one of the most popular vegetables that play a major role in boosting immunity against diseases. As a result of the successive population increase, many farmers resort to excessive use of chemical plant growth enhancers to increase the crop's productivity, which causes many health and environmental problems and reduces the sustainability of the soil. Microbial phytohormones and vitamins are safe, eco-friendly, and effective natural solutions to increase the crop's productivity and maintain the soil health at the same time. To our knowledge, until know there is no information about the roles of microbial gibberellic acid (GA3), and vitamins on the growth and quality traits of onions. Two field experiments were conducted during two consecutive winter seasons in a sandy calcareous soil farm. Two treatments were in the main plot (without and with microbial GA3), and six were in the sub-plot (control, chemical thiamine, ascorbic acid, riboflavin, and microbial ascorbic acid and riboflavin). Plant growth parameters including plant height, fresh weight, leaf number, bulb diameter, and neck diameter were recorded. Onion yield and their quality traits of sugar content, protein, antioxidants, vitamin C, phenols, flavonoids, and NPK were measured. The major findings revealed that plants treated with applications of microbial GA3 or vitamin treatments significantly improved the onion yield, phenotypic, physiological, and biochemical characteristics in both seasons. In the majority of the measured parameters, the microbial ascorbic acid treatment outperformed the other vitamin treatments. The combination of foliar microbial GA3 spray and vitamins, especially microbial ascorbic acid, and microbial riboflavin, produced the high onion yield, growth and quality traits of plant height, number of leaves, fresh weights, bulb diameter, sugar content, vitamin C, total antioxidants, total phenols, and flavonoids during both seasons. The application of microbial GA3 in combination with microbial vitamins as foliar spraying are promising eco-friendly, cheap, plant bio-stimulators that could be used safely in the field, especially under low-fertility soil, for good growth, yield, and high-quality onions.

Effect of tillage and precision nutrient placement on growth and productivity of maize (Zea mays)

The experiment was conducted at ICAR-Indian Agricultural Research Institute, New Delhi during rainy season of 2022 to asses the effect of tillage and precise nutrient placement on maize (Zea mays L.) growth. Employing a split-plot design with 3-tillage methods in main plots, viz. T1 , (conventional tillage); T2 , (once rotavator as minimum tillage) and T3 , (zero tillage) and 4, precision nutrient application options N1 , 50% RDF as point placement; N2 , 75% RDF as band placement; N3, 100% RDF as band placement and N4, 100% RDF as broadcasting were tested in subplots. Results indicated that tillage and precision nutrient placement practices improved the plant height and LAI of maize. Minimum tillage recorded significantly higher crop growth indices, viz. CGR (1.92 g/m2/day), RGR (26.80 g/g/day) and dry matter accumulation (204.58 g/plant) over other tillage practices at 60 to 90 Days after sowing (DAS). Root attributes also improved under minimum tillage. The grain yield was significantly higher with minimum tillage (6.22 t/ha) over other tillage practices. Among the precision nutrient application options, 100% RDF as band placement recorded significantly higher grain yield (6.20 t/ha) over N2 and N4 but remained statistically at par with 50% RDF point placement (6.06 t/ha). The findings suggested that adopting minimum tillage and precise nutrient point placement could significantly enhance maize growth and yield in kharif seasons, offering 50% reduction in fertilizer consumption.

Improving Water Use Efficiency, Yield, and Fruit Quality of Crimson Seedless Grapevines under Drought Stress

Drought stress is a group of abiotic stresses that affects plant growth and yield production. A field experiment over two successive seasons (2021–2022 and 2022–2023) in sand soil was conducted to investigate the integration effect of deficit irrigation, soil amendment “hundzsoil”, and the spraying of proline on the water use efficiency (WUE), yield, and fruit quality of 8-year-old Crimson seedless table grapes. Four application rates of soil amendment (0, 2, 4, and 6 kg hundzsoil /vine) were added during the dormancy period, and four irrigation levels at 125, 100, 75, and 60% of the field capacity were applied just before flowering until harvest. Proline at two levels (0 and 500 ppm) was applied as a foliar spray. Parameters such as bud fertility, weight of 100 berries, juice volume, and cluster number were positively affected by irrigation at 75% FC along with applying hundzsoil at 2 and 4 kg/vine under proline spray in both seasons. Irrigation at the 125% FC level with a 6 kg hundzsoil application under proline spray resulted in the highest yield, berries number, cluster length, cluster weight, and total anthocyanin in both seasons. The TSS/acidity ratio was significantly and positively affected by deficit irrigation (60% FC level) under hundzsoil at a rate of 4 kg alongside proline spray. Reducing irrigation to 60% FC without hundzsoil and proline spray negatively affected numerous growth parameters and the yield. However, irrigation at 60% FC alongside 6 kg of hundzsoil and proline showed the highest IWUE in both seasons. Proline spray was a key factor in conserving water used for irrigation. This study recommends using deficit irrigation alongside hundzsoil application under proline spray as an adequate strategy for water use efficiency and improving the yield and fruit quality of Crimson seedless grapevines cultivated in sand soil.

PLANTING OF STIMULATED CORN SEEDS AT DIFFERENT DATES IN SPRING SEASONS

This study was aimed to investigate the appropriate planting date and tolerance to adverse growing conditions in the spring season through seeds stimulation technology. A field experiment was carried out in the spring seasons of 2022 and 2023 according to a randomized complete block design in a split-plot arrangement with four replications. The main factor is planting dates (February 15th, March 1st and 15th, and April 1st and 15th). The secondary factor is seeds stimulation (potassium nitrate 6 mg L-1 + licorice extract 6 g L-1, as well as soaking with distilled water only). The stimulated seeds (potassium nitrate + licorice extract) and the planting date of February 15th were superior in traits of fertility ratio, number of grains per ear, weight of 300 grains, total grains yield, biological yield, and harvest index in both seasons (2022 and 2023). The stimulated seeds (potassium nitrate + licorice extract), planted on February 15th, excelled in the traits above (95.6 and 97.7%), (640.7 and 816.9 grain ear-1), (76.19 and 82.69 g), (7.2 and 7.8 ton ha-1), (17.5 and 18.9 ton ha-1) and (41.5 and 41.2%) in both seasons, respectively. It can be concluded that seeds stimulation improved the yield and its components of corn crop under a wide range of environmental conditions, and that planting the stimulated seeds on February 15th contributed to take advantage of the prevailing environmental conditions and regulating the relationship between source efficiency and sink capacity to obtain the highest grain yield. It can be recommended that soaking the seeds with potassium nitrate 6 mg L-1 + licorice extract 6 g L-1 together when planting in mid-February in the spring season to increase grains yield.

Effect of eggshell powder and phosphorus on growth and yield of MD7 groundnut variety in spring season in Phu Xuyen, Ha Noi

The experiment was conducted to evaluate the effect of eggshell powder and phosphorus on growth and yield of MD7 groundnut variety in the spring season in Phu Xuyen, Ha Noi. The two factor experiment was laid out in a split-plot design with three replications. The main plot consisted of three rates of eggshell powder (0, 300, 500 kg/ha). The sub-plot included three rates of phosphorus (30, 60, 90 kg/ha). The results showed that the growth and yield of plants fertilized with eggshell powder were higher than those with unfertilized eggshell powder. In addition, the growth traits and yield of MD7 groundnut variety increased with increasing phosphorus fertilizer doses from 30 kg P2O5/ha to 90 kg P 2O5/ha under the same fertilized or unfertilized eggshell powder conditions. Briefly, the results indicated that the rate of 500 kg eggshell powder/ha combined with 90 kg P2O5/ha was suitable for MD7 groundnut variety to grow, develop and produce the highest yield in the spring season in Phu Xuyen, Ha Noi.

Pruning techniques affect flowering, fruiting, yield and fruit biochemical traits in guava under transitory sub-tropical conditions

Production of quality fruits in the dry and low humid October–May period has been a challenge in the tropics and sub-tropics having wide weather fluctuations throughout the year. Henceforth, the research aimed at investigating the seasonal variations in vegetative developments as well as flowering, fruiting, yield, and fruit quality of guava emphasizing the off-seasonality by pruning 0 cm (control), 15 cm, 30 cm, and 45 cm from shoot-tip, once a year at spring (early March), monsoon (early June) and autumn (early September) under such atmospheric implications. Yearly and quarterly documentation at wet (June–August and September–November) and dry (December–February and March–May) seasons revealed that pruning in spring and autumn exhibited statistical parity for higher yearly yield of 31.71 kg and 31.58 kg plant−1, respectively. Moreover, spring pruning had maximum yield in the wet season (23.94 kg plant−1), while autumn pruning governed the dry season production (18.11 kg plant−1) having a notable wet period yield (13.47 kg plant−1). Considering the yearly and quarterly in March–May and December–February harvests, autumn pruning exhibited statistical supremacy for total soluble solids, titratable acidity, total sugar, vitamin C, and specific gravity. However, pruning time didn't influence the fruit physiochemical traits at the June–August and September–November quarters producing fruits of inferior quality compared to those of March–May and December–February harvests. On the other hand, pruning lengths of 30 cm and 45 cm demonstrated statistical consistency for auspicious vegetative, reproductive and fruit biochemical properties. Meanwhile, 30 cm pruning produced maximum number of flowers (224.71 plant−1) and fruits (155.89 plant−1), consequently the highest yield (38.38 kg plant−1). Treatment interactions too ascertained that off-season production of superior quality guava can be enhanced by 30 cm shoot-tip pruning in autumn without compromising the year-round harvests.

IMPACT OF FADAMA III ADDITIONAL FINANCE II ON YIELD AND INCOME OF FARO-44 RICE FARMERS BENEFICIARIES IN TARABA STATE, NIGERIA

The study was carried out to examine the impact of fadama III additional finance II on yield and income of faro-44 rice farmers beneficiaries in Taraba State, Nigeria. A Multi-stage sampling procedure was used to select 500 respondents. Data were analyzed using descriptive statistics (mean, frequency distribution and percentages) and inferential statistics (Gini coefficient). The findings of the study revealed that more days was expended in faro-44 rice weeding days per hectare with mean of 5.0 and 8.0 hours worked/day. The average number of tractors owned by the fadama users’ association (FUA) farmers was 2 with value of N3,129,079.63. The major inputs intervention received by the respondents were 5 kg seeds, wet season = 10 kg seeds all at N10,000/20 kg bag ((dry season), 2 bags of NPK fertilizer and 4 bags of NPK fertilizer at N7,000/50 kg bag in dry and wet season respectively, followed by a bag of Urea fertilizer in dry season and 2 bag of Urea fertilizer at N9,000/50 kg bag in wet season. Similarly, the respondents also received 4 liters of herbicides each in dry season and wet season. The result further revealed that the yield (kg/ha) in dry season increased from 3,675 kg before intervention to 6,156 kg after intervention representing 59.69% increase and with mean difference of 2,481 kg significant at P<0.01. Similarly, yields in wet season by the faro-44 rice farmers has a mean difference of 1,679 kg compared to mean difference of 2,481 yield in dry season. Consequently, income from dry season harvest (N/ha) increased by 40.77% representing Naira value from N787,375.00 to N1,720,400.00 for before and after fadama AF-II intervention. Similarly, income from wet season harvest only increased by 4.63% with wet season mean income difference of N315,189 compared to the dry season mean income difference of N933,025 implying that the respondents realized more income in dry season faro-44 rice farming than wet season in Taraba State, Nigeria. The study concluded that fadama III additional finance II project intervention had positive impact on yield and income of the respondents and faro-44 rice farming yielded more in dry season than wet season. It is recommended that farm inputs at subsidized rate should be made timely available, there should be further collaboration through the State fadama office with National Agricultural Land Development Authority (NALDA) to clear more land, rice promotion programmes like training on modern rice production techniques, new varieties of seeds in order to be technically efficient should also be promoted and farmers should be encouraged to form cooperatives to benefit more from such laudable fadama AF-II intervention and enjoy economies of scale.

Using a two-price market value framework to value differences in metabolizable energy concentration of pasture across seasons

CONTEXTInternationally, there have been focused efforts to develop methods of ranking pasture cultivars to assist livestock producers make informed decisions about investments in pastures. The Australian Forage Value Index (FVI) currently ranks cultivars for the dairy industry based on the value of differences in seasonal dry matter (DM) yield only. Metabolizable energy (ME) is the most important nutrient supplied by pasture for livestock production; its inclusion in the FVI may provide useful information for decision making. OBJECTIVEThe objective was to apply a two-price market value framework to value differences in both seasonal DM yield and ME concentration for valuing and ranking pasture cultivars. METHODSThe two-price market value framework allocates a replacement cost or salvage value to extra ME supplied by pasture during seasons when pasture is typically in deficit or surplus, respectively, of animal demand. The replacement cost and salvage value were calculated using a single (Method 1) or multiple (Method 2) nutritional components, and compared. The estimated market values for extra ME were combined with corresponding market values for extra DM yield and applied to a data set of 20 perennial ryegrass cultivars. Cultivars were ranked based on their extra annual market value compared to base cultivar Victorian. Rankings were first calculated based on the value of seasonal differences in DM yield, and then re-calculated using differences in both seasonal DM yield and ME concentration. RESULTS AND CONCLUSIONSReplacement costs of a MJ of ME of $0.023 and $0.010 were estimated for seasons of pasture deficit using Methods 1 and 2, with corresponding salvage values of $0.018 and $0.007 for seasons of pasture surplus. Compared with rankings based on the value of differences in DM yield alone, including the value of differences in ME concentrations had little effect on the rankings of individual cultivars. This result is strongly influenced by the fact that those cultivars that lead the ranking based on seasonal yield also have relatively high values for ME. SIGNIFICANCEThe two-price market value framework is one method for estimating the value of nutrients supplied by pasture and offers a simplified alternative to more common approaches for valuing pasture. The approach used here has potential for broader application and could be particularly useful for informing decisions about investments in research and innovation into traits which alter the nutritive concentrations of pastures swards and pasture species.

Design and Parametric Optimization Study of an Eccentric Parallelogram-Type Uprighting Device for Ratoon Rice Stubbles

To address the issue of reduced yield in the second season caused by damaged stubbles resulting from being compressed during the harvesting process of the first season’s ratoon rice, a device for rectifying the compressed stubbles was designed. Utilizing the DEM-MBD coupling simulation method, a simulation analysis was conducted to determine the range of key parameters and verify the feasibility of the solution. Using rotational speed, forward speed, and stubble entry angle as experimental factors and stubble rectification rate and second-season yield as evaluation metrics, a three-factor, three-level Box–Behnken response surface field trial was conducted. The theoretically optimal working parameter combination was found to be a forward speed of 1.4 m/s, device rotational speed of 75 rpm, and stubble entry angle of 39°. Under these conditions, three parallel experiments were performed, resulting in a rectification rate of 90.35% in the mechanically harvested and compressed area and a second-season yield of 2202.64 ± 35 kg/hm². The deviation from the numerical simulation results of parameter optimization was less than 5%. These findings suggest that the designed stubble rectification device for ratoon rice can meet the requirements of stubble rectification during the first-season harvest of ratoon rice. Furthermore, it provides valuable insights for reducing harvest losses in the first season and further improving the level of mechanized harvesting for ratoon rice.

Assessment of the nitrogen fertilizer split-application on maize grain yield and profitability on Nitisols of South-Kivu, Eastern D.R. Congo

Soil depletion constitutes a major challenge for agriculture and food security in highlands of eastern Democratic Republic of Congo (DRC). This study aimed to assess the effectiveness of the split-application of nitrogen fertilizer on grain yield and profitability of maize on Nitisols in eastern DRC. The urea fertilizer (100 kg ha−1) was applied in single, two, and three split-applications on three maize varieties for two cropping seasons. Results showed that maize growth and yield parameters varied significantly with N splitting strategy, varieties, and cropping season (p < 0.01). The single N application at the 45th day after sowing presented the highest grain yield (5.5 t ha−1) compared to split-applications for both cropping seasons. The variety 'ZM 627' had the highest grain yield in both seasons (5.4 and 5.8 t ha−1 for 2017 and 2018 cropping seasons, respectively). The benefit–cost ratio analysis showed that single application was more profitable, i.e. 1.63 USD kg−1 of fertilizer compared to two (0.6 USD kg−1) and three splits (0.22 USD kg−1 of fertilizer), though dependent on used varieties. The trend was the same for agronomic efficiency (AE); the single application yielded 11 kg kg−1 of maize grains. In addition, the split applications resulted in additional labor costs. Results from this study do not, therefore, recommend the N splitting strategy for maize on South-Kivu Nitisols.

Seed Security for Food Security: A Situation Analysis from the Rural Home Gardens

Seeds being a primary agricultural input, its availability, access, and affordability is directly linked to the agricultural production. Traditional home gardens, backyards, Kandyan forest gardens, and micro-agricultural systems rich in biodiversity and species, mix with the knowledge and skills of the community to manage sustainable food production systems. Therefore, the study targeted to identify the types of seed production and preservation systems available among the farming community, and to identify the impact of seed ownership on household resilience to food insecurity. Data were gathered using an initial transaction walk, and a series of focus group discussions with rural households of Imbulpe and Boralanda GN divisions of Rathnapura and Badulla districts, respectively. The study areas were selected given the diversity of the crops in the area. Sample profile included 30 farmers who were purposively selected from each GN division. Results were analyzed using descriptive statistics and it revealed the existence of two seed production and preservation systems: formal and informal. Formal seed systems included both private and public stakeholders in seed and planting material business. Farmers used these formal seed systems for commercial level cultivation of both up-country and low-country vegetables. Informal seed systems were characterized with the own production, selection, storage, and maintenance of seed banks, barter system, and community seed gifting/sharing, and purchasing locally produced seeds from the locality. Informal seed systems were identified mainly for cereals (traditional rice varieties), pulses, fruits, vegetables, and spices at home gardening level. This resulted in seed and planting material ownership retaining among the local farmers. Seed ownership is an intangible asset which empowers social and human capital status of the households. Locally bred varieties proved to be resilient to climate change and maintain the crop diversity in home gardens. Informally sourced seeds recorded comparatively low yield than the hybrid/improved seed varieties that are sourced from formal seed systems. In conclusion own seed supplies ensured food availability, minimized seasonal yield variations, and enhanced meal diversity.&#x0D; Keywords: Food security, Household resilience, Seed system

The coincidence of climate extremes with sensitive crop growth phases: Projected impact on sustainable crop water use and crop yield in the IGB river basins

Increased climate variability and extremes are unequivocal with unprecedented impacts on water resources and agriculture production systems. However, little is known about the impacts of climate extremes at the intra-seasonal level which remained largely unexplored. We investigated the coincidence of climate extremes with sensitive crop growth phases of wheat and rice in the Indus, Ganges and Brahmaputra (IGB) river basins of South Asia. We also quantified the related impacts on irrigation water demand (IWD), gross primary production (GPP) and crop yields (CY) simulated by a hydrological-vegetation model (LPJmL) during 1981–2100 using RCP4.5-SSP1 and RCP8.5-SSP3 framework. The climate extremes revealed a higher frequency and intensity during crop growth phases with significant increasing trends in future. Diverse changes in IWD, GPP and CY are projected in future under the influence of crop phase-specific extremes. The crop phase-specific changes in the IWD of wheat and rice will intensify in the future. More than 50 % of the change in future wheat irrigation is caused by warm and dry extremes during the ripening phase. Whereas, increase in IWD for rice is mainly associated with warm extremes only. The crop phase-specific GPP shows a decreasing trend in future for both wheat and rice in the Western part of IGB with the largest decrease during the reproductive phase of wheat (up to 36 %) and vegetative phase of rice (>20 %). This decrease is clearly reflected in seasonal yields i.e., both wheat (20 %) and rice (12 %) showed a decrease in future linked with warm and dry extremes. However, in the Eastern part of IGB, the GPP will mostly increase in future during the three crop phases of wheat and rice. These results can be used to help develop efficient adaptation strategies considering seasonal changes and sensitive crop phases for sustained food and water security in South Asia.

The role of crop simulation modeling in assessing potential climate change impacts

AbstractAgriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed.